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2D Case - Figures 12-13-15#
Convergence to reference solution for training performed using Adam and L-BFGS.
We investigate the effect of r-adaptivity and rh-adaptivity on the accuracy of displacement and von Mises stress.
#%% Libraries import
# import HiDeNN library
import sys
# sys.path.append("../neurom/")
from neurom.HiDeNN_PDE import MeshNN, NeuROM, MeshNN_2D, MeshNN_1D
# Import pre-processing functions
import neurom.src.Pre_processing as pre
# Import torch librairies
import torch
import torch.nn as nn
# Import mechanical functions
from neurom.src.PDE_Library import Strain, Stress,VonMises_plain_strain
# Import Training funcitons
from neurom.src.Training import Training_2D_FEM
#Import post processing libraries
import neurom.Post.Plots as Pplot
import time
import os
import torch._dynamo as dynamo
mps_device = torch.device("mps")
from importlib import reload # Python 3.4+
import tomllib
import numpy as numpy
import argparse
* Executing job in Configuration/config_2D.toml
* WARNING: could not load tikzplotlib
Default_config_file = 'Configurations/config_2D.toml'
with open(Default_config_file, mode="rb") as f:
config = tomllib.load(f)
# Prerequisites
mesh_resolution = [44, 144, 484, 1804, 7088 ]
eval_coord_file = "GroundTruth/eval_coordinates.npy"
num_displ_file = "GroundTruth/num_displacement.npy"
num_VM_stress_file = "GroundTruth/num_VM_stress.npy"
eval_coord = torch.tensor(numpy.load(eval_coord_file), dtype=torch.float64, requires_grad=True)
num_displ = torch.tensor(numpy.load(num_displ_file))
num_VM_stress = torch.tensor(numpy.load(num_VM_stress_file))
# Experiment
element_size = [2.0, 1.0, 0.5, 0.25, 0.125]
config["solver"]["FrozenMesh"] = True
optimizers = ["adam","lbfgs"]
error_u = numpy.zeros((len(element_size),len(optimizers)))
error_v = numpy.zeros((len(element_size),len(optimizers)))
error_stress = numpy.zeros((len(element_size),len(optimizers)))
error_stress_max = numpy.zeros((len(element_size),len(optimizers)))
for e in range(len(element_size)):
config["interpolation"]["MaxElemSize2D"] = element_size[e]
for op in range(len(optimizers)):
config["training"]["optimizer"] = optimizers[op]
if config["interpolation"]["dimension"] == 1:
Mat = pre.Material( flag_lame = True, # If True should input lmbda and mu instead of E and nu
coef1 = config["material"]["E"], # Young Modulus
coef2 = config["geometry"]["A"] # Section area of the 1D bar
)
elif config["interpolation"]["dimension"] == 2:
try:
Mat = pre.Material( flag_lame = False, # If True should input lmbda and mu instead of E and nu
coef1 = config["material"]["E"], # Young Modulus
coef2 = config["material"]["nu"] # Poisson's ratio
)
except:
Mat = pre.Material( flag_lame = True, # If True should input lmbda and mu instead of E and nu
coef1 = config["material"]["lmbda"], # First Lame's coef
coef2 = config["material"]["mu"] # Second Lame's coef
)
MaxElemSize = pre.ElementSize(
dimension = config["interpolation"]["dimension"],
L = config["geometry"]["L"],
order = config["interpolation"]["order"],
np = config["interpolation"]["np"],
MaxElemSize2D = config["interpolation"]["MaxElemSize2D"]
)
Excluded = []
Mesh_object = pre.Mesh(
config["geometry"]["Name"], # Create the mesh object
MaxElemSize,
config["interpolation"]["order"],
config["interpolation"]["dimension"]
)
Mesh_object.AddBorders(config["Borders"]["Borders"])
Mesh_object.AddBCs( # Include Boundary physical domains infos (BCs+volume)
config["geometry"]["Volume_element"],
Excluded,
config["DirichletDictionryList"]
)
Mesh_object.MeshGeo() # Mesh the .geo file if .msh does not exist
Mesh_object.ReadMesh() # Parse the .msh file
Mesh_object.ExportMeshVtk()
if int(Mesh_object.dim) != int(Mesh_object.dimension):
raise ValueError("The dimension of the provided geometry does not match the job dimension")
Model_FEM = MeshNN_2D(Mesh_object, n_components = 2)
Model_FEM.Freeze_Mesh()
Model_FEM.UnFreeze_FEM()
if not config["solver"]["FrozenMesh"]:
Model_FEM.UnFreeze_Mesh()
Model_FEM.RefinementParameters( MaxGeneration = config["training"]["h_adapt_MaxGeneration"],
Jacobian_threshold = config["training"]["h_adapt_J_thrshld"])
Model_FEM.TrainingParameters( loss_decrease_c = config["training"]["loss_decrease_c"],
Max_epochs = config["training"]["n_epochs"],
learning_rate = config["training"]["learning_rate"])
Model_FEM = Training_2D_FEM(Model_FEM, config, Mat)
# evaluation
Model_FEM.eval()
elem_IDs = torch.tensor(Model_FEM.mesh.GetCellIds(eval_coord),dtype=torch.int)
u = Model_FEM(eval_coord, elem_IDs)
eps = Strain(u,eval_coord)
sigma = torch.stack(Stress(eps[:,0], eps[:,1], eps[:,2], Mat.lmbda, Mat.mu),dim=1)
sigma_VM = VonMises_plain_strain(sigma, Mat.lmbda, Mat.mu)
error_u[e,op] = (torch.linalg.vector_norm(num_displ[:,0] - u[0,:])/torch.linalg.vector_norm(num_displ[:,0])).item()
error_v[e,op] = (torch.linalg.vector_norm(num_displ[:,1] - u[1,:])/torch.linalg.vector_norm(num_displ[:,1])).item()
error_stress[e,op] = (torch.linalg.vector_norm(num_VM_stress - sigma_VM)/torch.linalg.vector_norm(num_VM_stress)).item()
error_stress_max[e,op] = (max(sigma_VM)/max(num_VM_stress)).item()
_ _ ____ ___ __ __
| \ | | ___ _ _| _ \ / _ \| \/ |
| \| |/ _ \ | | | |_) | | | | |\/| |
| |\ | __/ |_| | _ <| |_| | | | |
|_| \_|\___|\__,_|_| \_\ ___/|_| |_|
2024.09.18
************ MESH READING COMPLETE ************
* Dimension of the problem: 2D
* Elements type: t3: 3-node triangle
* Number of Dofs: 88
* Refinement level: 0
**************** START TRAINING ***************
epoch 2 loss = 6.4658e+00
epoch 50 loss = 5.3128e+00
epoch 100 loss = 4.3189e+00
epoch 150 loss = 3.5167e+00
epoch 200 loss = 2.8793e+00
epoch 250 loss = 2.3809e+00
epoch 300 loss = 1.9973e+00
epoch 350 loss = 1.7064e+00
epoch 400 loss = 1.4891e+00
epoch 450 loss = 1.3290e+00
epoch 500 loss = 1.2127e+00
epoch 550 loss = 1.1292e+00
epoch 600 loss = 1.0699e+00
epoch 650 loss = 1.0283e+00
epoch 700 loss = 9.9946e-01
epoch 750 loss = 9.7968e-01
epoch 800 loss = 9.6627e-01
epoch 850 loss = 9.5729e-01
epoch 900 loss = 9.5137e-01
epoch 950 loss = 9.4750e-01
epoch 1000 loss = 9.4502e-01
epoch 1050 loss = 9.4345e-01
epoch 1100 loss = 9.4247e-01
epoch 1150 loss = 9.4188e-01
epoch 1200 loss = 9.4151e-01
epoch 1250 loss = 9.4130e-01
epoch 1300 loss = 9.4118e-01
epoch 1350 loss = 9.4111e-01
epoch 1400 loss = 9.4107e-01
epoch 1450 loss = 9.4105e-01
epoch 1500 loss = 9.4104e-01
epoch 1538 loss = 9.4103e-01
*************** END FIRST PHASE ***************
* Training time: 2.6587867736816406s
* Average epoch time: 0.0017287300218996363s
***************** END TRAINING ****************
* Training time: 2.6587867736816406s
_ _ ____ ___ __ __
| \ | | ___ _ _| _ \ / _ \| \/ |
| \| |/ _ \ | | | |_) | | | | |\/| |
| |\ | __/ |_| | _ <| |_| | | | |
|_| \_|\___|\__,_|_| \_\ ___/|_| |_|
2024.09.18
************ MESH READING COMPLETE ************
* Dimension of the problem: 2D
* Elements type: t3: 3-node triangle
* Number of Dofs: 88
* Refinement level: 0
**************** START TRAINING ***************
epoch 2 loss = 9.4117e-01
epoch 3 loss = 9.4102e-01
epoch 4 loss = 9.4102e-01
*************** END FIRST PHASE ***************
* Training time: 0.037068843841552734s
* Average epoch time: 0.009267210960388184s
***************** END TRAINING ****************
* Training time: 0.037068843841552734s
_ _ ____ ___ __ __
| \ | | ___ _ _| _ \ / _ \| \/ |
| \| |/ _ \ | | | |_) | | | | |\/| |
| |\ | __/ |_| | _ <| |_| | | | |
|_| \_|\___|\__,_|_| \_\ ___/|_| |_|
2024.09.18
************ MESH READING COMPLETE ************
* Dimension of the problem: 2D
* Elements type: t3: 3-node triangle
* Number of Dofs: 288
* Refinement level: 0
**************** START TRAINING ***************
epoch 2 loss = 1.5138e+01
epoch 50 loss = 1.2401e+01
epoch 100 loss = 1.0017e+01
epoch 150 loss = 8.0260e+00
epoch 200 loss = 6.3891e+00
epoch 250 loss = 5.0635e+00
epoch 300 loss = 4.0069e+00
epoch 350 loss = 3.1785e+00
epoch 400 loss = 2.5403e+00
epoch 450 loss = 2.0575e+00
epoch 500 loss = 1.6992e+00
epoch 550 loss = 1.4385e+00
epoch 600 loss = 1.2527e+00
epoch 650 loss = 1.1228e+00
epoch 700 loss = 1.0340e+00
epoch 750 loss = 9.7459e-01
epoch 800 loss = 9.3562e-01
epoch 850 loss = 9.1062e-01
epoch 900 loss = 8.9493e-01
epoch 950 loss = 8.8528e-01
epoch 1000 loss = 8.7948e-01
epoch 1050 loss = 8.7607e-01
epoch 1100 loss = 8.7410e-01
epoch 1150 loss = 8.73e-01
epoch 1200 loss = 8.7239e-01
epoch 1250 loss = 8.7206e-01
epoch 1300 loss = 8.7188e-01
epoch 1350 loss = 8.7179e-01
epoch 1400 loss = 8.7175e-01
epoch 1427 loss = 8.7174e-01
*************** END FIRST PHASE ***************
* Training time: 2.530526876449585s
* Average epoch time: 0.0017733194649261282s
***************** END TRAINING ****************
* Training time: 2.530526876449585s
_ _ ____ ___ __ __
| \ | | ___ _ _| _ \ / _ \| \/ |
| \| |/ _ \ | | | |_) | | | | |\/| |
| |\ | __/ |_| | _ <| |_| | | | |
|_| \_|\___|\__,_|_| \_\ ___/|_| |_|
2024.09.18
************ MESH READING COMPLETE ************
* Dimension of the problem: 2D
* Elements type: t3: 3-node triangle
* Number of Dofs: 288
* Refinement level: 0
**************** START TRAINING ***************
epoch 2 loss = 9.8282e-01
epoch 3 loss = 8.7197e-01
epoch 4 loss = 8.7171e-01
epoch 5 loss = 8.7171e-01
*************** END FIRST PHASE ***************
* Training time: 0.09317278861999512s
* Average epoch time: 0.018634557723999023s
***************** END TRAINING ****************
* Training time: 0.09317278861999512s
_ _ ____ ___ __ __
| \ | | ___ _ _| _ \ / _ \| \/ |
| \| |/ _ \ | | | |_) | | | | |\/| |
| |\ | __/ |_| | _ <| |_| | | | |
|_| \_|\___|\__,_|_| \_\ ___/|_| |_|
2024.09.18
************ MESH READING COMPLETE ************
* Dimension of the problem: 2D
* Elements type: t3: 3-node triangle
* Number of Dofs: 968
* Refinement level: 0
**************** START TRAINING ***************
epoch 2 loss = 2.6950e+01
epoch 50 loss = 2.2063e+01
epoch 100 loss = 1.7802e+01
epoch 150 loss = 1.4224e+01
epoch 200 loss = 1.1257e+01
epoch 250 loss = 8.8328e+00
epoch 300 loss = 6.8811e+00
epoch 350 loss = 5.3346e+00
epoch 400 loss = 4.1295e+00
epoch 450 loss = 3.2067e+00
epoch 500 loss = 2.5129e+00
epoch 550 loss = 2.0012e+00
epoch 600 loss = 1.6312e+00
epoch 650 loss = 1.3691e+00
epoch 700 loss = 1.1872e+00
epoch 750 loss = 1.0636e+00
epoch 800 loss = 9.8143e-01
epoch 850 loss = 9.2795e-01
epoch 900 loss = 8.9391e-01
epoch 950 loss = 8.7270e-01
epoch 1000 loss = 8.5978e-01
epoch 1050 loss = 8.5208e-01
epoch 1100 loss = 8.4759e-01
epoch 1150 loss = 8.4503e-01
epoch 1200 loss = 8.4360e-01
epoch 1250 loss = 8.4282e-01
epoch 1300 loss = 8.4241e-01
epoch 1350 loss = 8.4219e-01
epoch 1400 loss = 8.4208e-01
epoch 1450 loss = 8.4202e-01
epoch 1500 loss = 8.42e-01
epoch 1533 loss = 8.4199e-01
*************** END FIRST PHASE ***************
* Training time: 3.1704509258270264s
* Average epoch time: 0.0020681349809700106s
***************** END TRAINING ****************
* Training time: 3.1704509258270264s
_ _ ____ ___ __ __
| \ | | ___ _ _| _ \ / _ \| \/ |
| \| |/ _ \ | | | |_) | | | | |\/| |
| |\ | __/ |_| | _ <| |_| | | | |
|_| \_|\___|\__,_|_| \_\ ___/|_| |_|
2024.09.18
************ MESH READING COMPLETE ************
* Dimension of the problem: 2D
* Elements type: t3: 3-node triangle
* Number of Dofs: 968
* Refinement level: 0
**************** START TRAINING ***************
epoch 2 loss = 1.4611e+00
epoch 3 loss = 9.7258e-01
epoch 4 loss = 8.4431e-01
epoch 5 loss = 8.4229e-01
epoch 6 loss = 8.4197e-01
epoch 7 loss = 8.4197e-01
epoch 8 loss = 8.4197e-01
*************** END FIRST PHASE ***************
* Training time: 0.22775816917419434s
* Average epoch time: 0.028469771146774292s
***************** END TRAINING ****************
* Training time: 0.22775816917419434s
_ _ ____ ___ __ __
| \ | | ___ _ _| _ \ / _ \| \/ |
| \| |/ _ \ | | | |_) | | | | |\/| |
| |\ | __/ |_| | _ <| |_| | | | |
|_| \_|\___|\__,_|_| \_\ ___/|_| |_|
2024.09.18
************ MESH READING COMPLETE ************
* Dimension of the problem: 2D
* Elements type: t3: 3-node triangle
* Number of Dofs: 3608
* Refinement level: 0
**************** START TRAINING ***************
epoch 2 loss = 5.3351e+01
epoch 50 loss = 4.3758e+01
epoch 100 loss = 3.5235e+01
epoch 150 loss = 2.8066e+01
epoch 200 loss = 2.2113e+01
epoch 250 loss = 1.7236e+01
epoch 300 loss = 1.3296e+01
epoch 350 loss = 1.0159e+01
epoch 400 loss = 7.7022e+00
epoch 450 loss = 5.8096e+00
epoch 500 loss = 4.3774e+00
epoch 550 loss = 3.3133e+00
epoch 600 loss = 2.5377e+00
epoch 650 loss = 1.9833e+00
epoch 700 loss = 1.5948e+00
epoch 750 loss = 1.3281e+00
epoch 800 loss = 1.1487e+00
epoch 850 loss = 1.0305e+00
epoch 900 loss = 9.5413e-01
epoch 950 loss = 9.0584e-01
epoch 1000 loss = 8.7591e-01
epoch 1050 loss = 8.5774e-01
epoch 1100 loss = 8.4692e-01
epoch 1150 loss = 8.4061e-01
epoch 1200 loss = 8.37e-01
epoch 1250 loss = 8.3498e-01
epoch 1300 loss = 8.3386e-01
epoch 1350 loss = 8.3326e-01
epoch 1400 loss = 8.3294e-01
epoch 1450 loss = 8.3278e-01
epoch 1499 loss = 8.3270e-01
*************** END FIRST PHASE ***************
* Training time: 5.19927978515625s
* Average epoch time: 0.0034684988560081723s
***************** END TRAINING ****************
* Training time: 5.19927978515625s
_ _ ____ ___ __ __
| \ | | ___ _ _| _ \ / _ \| \/ |
| \| |/ _ \ | | | |_) | | | | |\/| |
| |\ | __/ |_| | _ <| |_| | | | |
|_| \_|\___|\__,_|_| \_\ ___/|_| |_|
2024.09.18
************ MESH READING COMPLETE ************
* Dimension of the problem: 2D
* Elements type: t3: 3-node triangle
* Number of Dofs: 3608
* Refinement level: 0
**************** START TRAINING ***************
epoch 2 loss = 2.9088e+00
epoch 3 loss = 1.4534e+00
epoch 4 loss = 1.1399e+00
epoch 5 loss = 9.5209e-01
epoch 6 loss = 8.4292e-01
epoch 7 loss = 8.3496e-01
epoch 8 loss = 8.3363e-01
epoch 9 loss = 8.3295e-01
epoch 10 loss = 8.3262e-01
epoch 11 loss = 8.3261e-01
epoch 12 loss = 8.3261e-01
epoch 13 loss = 8.3261e-01
*************** END FIRST PHASE ***************
* Training time: 0.7203071117401123s
* Average epoch time: 0.05540823936462402s
***************** END TRAINING ****************
* Training time: 0.7203071117401123s
_ _ ____ ___ __ __
| \ | | ___ _ _| _ \ / _ \| \/ |
| \| |/ _ \ | | | |_) | | | | |\/| |
| |\ | __/ |_| | _ <| |_| | | | |
|_| \_|\___|\__,_|_| \_\ ___/|_| |_|
2024.09.18
************ MESH READING COMPLETE ************
* Dimension of the problem: 2D
* Elements type: t3: 3-node triangle
* Number of Dofs: 14176
* Refinement level: 0
**************** START TRAINING ***************
epoch 2 loss = 1.1024e+02
epoch 50 loss = 9.0231e+01
epoch 100 loss = 7.2811e+01
epoch 150 loss = 5.8143e+01
epoch 200 loss = 4.5907e+01
epoch 250 loss = 3.583e+01
epoch 300 loss = 2.7644e+01
epoch 350 loss = 2.1091e+01
epoch 400 loss = 1.5924e+01
epoch 450 loss = 1.1916e+01
epoch 500 loss = 8.8577e+00
epoch 550 loss = 6.5632e+00
epoch 600 loss = 4.8713e+00
epoch 650 loss = 3.645e+00
epoch 700 loss = 2.7709e+00
epoch 750 loss = 2.1579e+00
epoch 800 loss = 1.7342e+00
epoch 850 loss = 1.4451e+00
epoch 900 loss = 1.2497e+00
epoch 950 loss = 1.1185e+00
epoch 1000 loss = 1.0307e+00
epoch 1050 loss = 9.7155e-01
epoch 1100 loss = 9.3147e-01
epoch 1150 loss = 9.0387e-01
epoch 1200 loss = 8.8455e-01
epoch 1250 loss = 8.7075e-01
epoch 1300 loss = 8.6070e-01
epoch 1350 loss = 8.5326e-01
epoch 1400 loss = 8.477e-01
epoch 1450 loss = 8.4347e-01
epoch 1500 loss = 8.4026e-01
epoch 1550 loss = 8.3781e-01
epoch 1600 loss = 8.3593e-01
epoch 1650 loss = 8.3450e-01
epoch 1700 loss = 8.3343e-01
epoch 1750 loss = 8.326e-01
epoch 1800 loss = 8.3197e-01
epoch 1850 loss = 8.3149e-01
epoch 1900 loss = 8.3113e-01
epoch 1950 loss = 8.3087e-01
epoch 2000 loss = 8.3067e-01
epoch 2037 loss = 8.3055e-01
*************** END FIRST PHASE ***************
* Training time: 12.15532922744751s
* Average epoch time: 0.00596727011656726s
***************** END TRAINING ****************
* Training time: 12.15532922744751s
_ _ ____ ___ __ __
| \ | | ___ _ _| _ \ / _ \| \/ |
| \| |/ _ \ | | | |_) | | | | |\/| |
| |\ | __/ |_| | _ <| |_| | | | |
|_| \_|\___|\__,_|_| \_\ ___/|_| |_|
2024.09.18
************ MESH READING COMPLETE ************
* Dimension of the problem: 2D
* Elements type: t3: 3-node triangle
* Number of Dofs: 14176
* Refinement level: 0
**************** START TRAINING ***************
epoch 2 loss = 6.7976e+00
epoch 3 loss = 3.1008e+00
epoch 4 loss = 1.7886e+00
epoch 5 loss = 1.4736e+00
epoch 6 loss = 1.2995e+00
epoch 7 loss = 1.1571e+00
epoch 8 loss = 1.0543e+00
epoch 9 loss = 9.7230e-01
epoch 10 loss = 8.6787e-01
epoch 11 loss = 8.4077e-01
epoch 12 loss = 8.3447e-01
epoch 13 loss = 8.3255e-01
epoch 14 loss = 8.3175e-01
epoch 15 loss = 8.3114e-01
epoch 16 loss = 8.3071e-01
epoch 17 loss = 8.3042e-01
epoch 18 loss = 8.3012e-01
epoch 19 loss = 8.3002e-01
epoch 20 loss = 8.3000e-01
epoch 21 loss = 8.3e-01
epoch 22 loss = 8.3e-01
epoch 23 loss = 8.3e-01
epoch 24 loss = 8.3e-01
*************** END FIRST PHASE ***************
* Training time: 3.0665628910064697s
* Average epoch time: 0.12777345379193625s
***************** END TRAINING ****************
* Training time: 3.0665628910064697s
print("Adam")
print("u = ", error_u[:,0])
print("v = ", error_v[:,0])
print("s = ", error_stress[:,0])
print("s max = ", error_stress_max[:,0])
print()
print("LBFGS")
print("u = ", error_u[:,1])
print("v = ", error_v[:,1])
print("s = ", error_stress[:,1])
print("s max = ", error_stress_max[:,1])
Adam
u = [0.1878323 0.10327197 0.0385544 0.02584601 0.08669397]
v = [0.02597361 0.01095467 0.0034613 0.00133766 0.00227587]
s = [0.2297074 0.14719949 0.08269867 0.04360195 0.02777556]
s max = [0.65601306 0.77541657 0.93797908 0.95440446 1.00013897]
LBFGS
u = [0.18588991 0.10080447 0.03665099 0.00976878 0.00170569]
v = [0.02586959 0.01065384 0.00328927 0.00084035 0.00015746]
s = [0.22989806 0.14733525 0.08272104 0.04314683 0.02197674]
s max = [0.65594826 0.77632042 0.93821257 0.95494388 0.99343906]
import matplotlib.pyplot as plt
import matplotlib
plt.rcParams['text.usetex'] = False
# Plot normalized displacement error
fig = matplotlib.pyplot.gcf()
ax = plt.gca()
plt.plot(mesh_resolution, error_u[:,0],'--', color = "darkblue", label = r'$\| e_{u_x}\|_2$'+ ", Adam")
plt.plot(mesh_resolution, error_v[:,0],'--', color = "purple", label = r'$\| e_{u_y}\|_2$'+ ", Adam")
plt.plot(mesh_resolution, error_stress[:,0],'--', color = "green", label = r'$\| e_{\sigma_{VM}}\|_2$'+ ", Adam")
plt.plot(mesh_resolution, error_u[:,1],'-', color = "darkblue", label = r'$\| e_{u_x}\|_2$'+ ", L-BFGS")
plt.plot(mesh_resolution, error_v[:,1],'-', color = "purple", label = r'$\| e_{u_y}\|_2$'+ ", L-BFGS")
plt.plot(mesh_resolution, error_stress[:,1],'-', color = "green", label = r'$\| e_{\sigma_{VM}}\|_2$'+ ", L-BFGS")
ax.set_xscale('log')
ax.set_yscale('log')
plt.xlabel("Number of mesh nodes")
plt.ylabel("Normalized displacement error")
plt.legend(loc='center left', bbox_to_anchor=(1, 0.5),frameon=False )
plt.show()
# Prerequisites
mesh_resolution = [44, 144, 484, 1804, 7088 ]
# eval_coord_file = "../2D_example/eval_coordinates.npy"
# num_displ_file = "../2D_example/num_solution/num_displacement.npy"
# num_VM_stress_file = "../2D_example/num_solution/num_VM_stress.npy"
eval_coord_file = "GroundTruth/eval_coordinates.npy"
num_displ_file = "GroundTruth/num_displacement.npy"
num_VM_stress_file = "GroundTruth/num_VM_stress.npy"
eval_coord = torch.tensor(numpy.load(eval_coord_file), dtype=torch.float64, requires_grad=True)
num_displ = torch.tensor(numpy.load(num_displ_file))
num_VM_stress = torch.tensor(numpy.load(num_VM_stress_file))
# Experiment
element_size = 2.0
config["solver"]["FrozenMesh"] = False
optimizers = ["adam","lbfgs"]
refinment = [1,2,3,4,5]
config["interpolation"]["MaxElemSize2D"] = element_size
r_adapt_error_u = numpy.zeros((len(refinment),len(optimizers)))
r_adapt_error_v = numpy.zeros((len(refinment),len(optimizers)))
r_adapt_error_stress = numpy.zeros((len(refinment),len(optimizers)))
r_adapt_error_stress_max = numpy.zeros((len(refinment),len(optimizers)))
for e in range(len(refinment)):
config["training"]["multiscl_max_refinment"] = refinment[e]
for op in range(len(optimizers)):
config["training"]["optimizer"] = optimizers[op]
Mat = pre.Material( flag_lame = True, # If True should input lmbda and mu instead of E and nu
coef1 = config["material"]["lmbda"], # Young Modulus
coef2 = config["material"]["mu"] # Poisson's ratio
)
MaxElemSize = pre.ElementSize(
dimension = config["interpolation"]["dimension"],
L = config["geometry"]["L"],
order = config["interpolation"]["order"],
np = config["interpolation"]["np"],
MaxElemSize2D = config["interpolation"]["MaxElemSize2D"]
)
Excluded = []
Mesh_object = pre.Mesh(
config["geometry"]["Name"], # Create the mesh object
MaxElemSize,
config["interpolation"]["order"],
config["interpolation"]["dimension"]
)
Mesh_object.AddBorders(config["Borders"]["Borders"])
Mesh_object.AddBCs( # Include Boundary physical domains infos (BCs+volume)
config["geometry"]["Volume_element"],
Excluded,
config["DirichletDictionryList"]
)
Mesh_object.MeshGeo() # Mesh the .geo file if .msh does not exist
Mesh_object.ReadMesh() # Parse the .msh file
Mesh_object.ExportMeshVtk()
if int(Mesh_object.dim) != int(Mesh_object.dimension):
raise ValueError("The dimension of the provided geometry does not match the job dimension")
Model_FEM = MeshNN_2D(Mesh_object, n_components = 2)
Model_FEM.Freeze_Mesh()
Model_FEM.UnFreeze_FEM()
if not config["solver"]["FrozenMesh"]:
Model_FEM.UnFreeze_Mesh()
Model_FEM.RefinementParameters( MaxGeneration = config["training"]["h_adapt_MaxGeneration"],
Jacobian_threshold = config["training"]["h_adapt_J_thrshld"])
Model_FEM.TrainingParameters( loss_decrease_c = config["training"]["loss_decrease_c"],
Max_epochs = config["training"]["n_epochs"],
learning_rate = config["training"]["learning_rate"])
Model_FEM = Training_2D_FEM(Model_FEM, config, Mat)
# evaluation
Model_FEM.eval()
# Model_FEM.mesh.Nodes = [[i+1,Model_FEM.coordinates[i][0][0].item(),Model_FEM.coordinates[i][0][1].item(),0] for i in range(len(Model_FEM.coordinates))]
coordinates_all = torch.ones_like(Model_FEM.coordinates_all)
coordinates_all[Model_FEM.coord_free] = Model_FEM.coordinates['free']
coordinates_all[~Model_FEM.coord_free] = Model_FEM.coordinates['imposed']
Nodes = torch.hstack([torch.linspace(1,coordinates_all.shape[0],coordinates_all.shape[0], dtype = coordinates_all.dtype, device = coordinates_all.device)[:,None],
coordinates_all])
Nodes = torch.hstack([Nodes,torch.zeros(Nodes.shape[0],1, dtype = Nodes.dtype, device = Nodes.device)])
Model_FEM.mesh.Nodes = Nodes.detach().cpu().numpy()
Model_FEM.mesh.Connectivity = Model_FEM.connectivity
Model_FEM.mesh.ExportMeshVtk(flag_update = True)
elem_IDs = torch.tensor(Model_FEM.mesh.GetCellIds(eval_coord),dtype=torch.int)
u = Model_FEM(eval_coord, elem_IDs)
eps = Strain(u,eval_coord)
sigma = torch.stack(Stress(eps[:,0], eps[:,1], eps[:,2], Mat.lmbda, Mat.mu),dim=1)
sigma_VM = VonMises_plain_strain(sigma, Mat.lmbda, Mat.mu)
r_adapt_error_u[e,op] = (torch.linalg.vector_norm(num_displ[:,0] - u[0,:])/torch.linalg.vector_norm(num_displ[:,0])).item()
r_adapt_error_v[e,op] = (torch.linalg.vector_norm(num_displ[:,1] - u[1,:])/torch.linalg.vector_norm(num_displ[:,1])).item()
r_adapt_error_stress[e,op] = (torch.linalg.vector_norm(num_VM_stress - sigma_VM)/torch.linalg.vector_norm(num_VM_stress)).item()
r_adapt_error_stress_max[e,op] = (max(sigma_VM)/max(num_VM_stress)).item()
_ _ ____ ___ __ __
| \ | | ___ _ _| _ \ / _ \| \/ |
| \| |/ _ \ | | | |_) | | | | |\/| |
| |\ | __/ |_| | _ <| |_| | | | |
|_| \_|\___|\__,_|_| \_\ ___/|_| |_|
2024.09.18
************ MESH READING COMPLETE ************
* Dimension of the problem: 2D
* Elements type: t3: 3-node triangle
* Number of Dofs: 88
* Refinement level: 0
**************** START TRAINING ***************
epoch 2 loss = 6.4619e+00
epoch 50 loss = 5.1704e+00
epoch 100 loss = 4.1221e+00
epoch 150 loss = 3.3192e+00
epoch 200 loss = 2.7077e+00
epoch 250 loss = 2.2451e+00
epoch 300 loss = 1.8976e+00
epoch 350 loss = 1.6385e+00
epoch 400 loss = 1.4464e+00
epoch 450 loss = 1.3049e+00
epoch 500 loss = 1.2011e+00
epoch 550 loss = 1.1253e+00
epoch 600 loss = 1.0702e+00
epoch 650 loss = 1.0304e+00
epoch 700 loss = 1.0018e+00
epoch 750 loss = 9.814e-01
epoch 800 loss = 9.6694e-01
epoch 850 loss = 9.5677e-01
epoch 900 loss = 9.4965e-01
epoch 950 loss = 9.447e-01
epoch 1000 loss = 9.4126e-01
epoch 1050 loss = 9.3889e-01
epoch 1100 loss = 9.3724e-01
epoch 1150 loss = 9.361e-01
epoch 1200 loss = 9.353e-01
epoch 1250 loss = 9.3473e-01
epoch 1300 loss = 9.3431e-01
epoch 1350 loss = 9.3401e-01
epoch 1400 loss = 9.3378e-01
epoch 1450 loss = 9.336e-01
epoch 1500 loss = 9.3345e-01
epoch 1550 loss = 9.3333e-01
epoch 1600 loss = 9.3323e-01
epoch 1650 loss = 9.3315e-01
epoch 1700 loss = 9.3308e-01
epoch 1750 loss = 9.3301e-01
epoch 1800 loss = 9.3295e-01
epoch 1850 loss = 9.329e-01
epoch 1900 loss = 9.3285e-01
epoch 1950 loss = 9.3281e-01
epoch 2000 loss = 9.3276e-01
epoch 2050 loss = 9.3273e-01
epoch 2100 loss = 9.3269e-01
epoch 2150 loss = 9.3265e-01
epoch 2200 loss = 9.3261e-01
epoch 2250 loss = 9.3258e-01
epoch 2300 loss = 9.3254e-01
epoch 2350 loss = 9.3251e-01
epoch 2400 loss = 9.3247e-01
epoch 2450 loss = 9.3244e-01
epoch 2500 loss = 9.3240e-01
epoch 2550 loss = 9.3237e-01
epoch 2600 loss = 9.3233e-01
epoch 2650 loss = 9.3229e-01
epoch 2700 loss = 9.3226e-01
epoch 2750 loss = 9.3222e-01
epoch 2800 loss = 9.3218e-01
epoch 2850 loss = 9.3215e-01
epoch 2900 loss = 9.3211e-01
epoch 2950 loss = 9.3207e-01
epoch 3000 loss = 9.3204e-01
epoch 3050 loss = 9.32e-01
epoch 3100 loss = 9.3196e-01
epoch 3150 loss = 9.3192e-01
epoch 3200 loss = 9.3188e-01
epoch 3250 loss = 9.3185e-01
epoch 3300 loss = 9.3181e-01
epoch 3350 loss = 9.3177e-01
epoch 3400 loss = 9.3173e-01
epoch 3450 loss = 9.3169e-01
epoch 3500 loss = 9.3165e-01
epoch 3550 loss = 9.3161e-01
epoch 3600 loss = 9.3158e-01
epoch 3650 loss = 9.3154e-01
epoch 3700 loss = 9.315e-01
epoch 3750 loss = 9.3146e-01
epoch 3800 loss = 9.3142e-01
epoch 3850 loss = 9.3139e-01
epoch 3900 loss = 9.3135e-01
epoch 3950 loss = 9.3131e-01
epoch 4000 loss = 9.3127e-01
epoch 4050 loss = 9.3124e-01
epoch 4100 loss = 9.3120e-01
epoch 4150 loss = 9.3117e-01
epoch 4200 loss = 9.3113e-01
epoch 4250 loss = 9.311e-01
epoch 4300 loss = 9.3106e-01
epoch 4350 loss = 9.3103e-01
epoch 4400 loss = 9.3099e-01
epoch 4450 loss = 9.3096e-01
epoch 4500 loss = 9.3093e-01
epoch 4550 loss = 9.309e-01
epoch 4600 loss = 9.3087e-01
epoch 4650 loss = 9.3084e-01
epoch 4700 loss = 9.3081e-01
epoch 4750 loss = 9.3078e-01
epoch 4800 loss = 9.3075e-01
epoch 4850 loss = 9.3072e-01
epoch 4900 loss = 9.3069e-01
epoch 4950 loss = 9.3067e-01
epoch 5000 loss = 9.3064e-01
epoch 5050 loss = 9.3062e-01
epoch 5100 loss = 9.3059e-01
epoch 5150 loss = 9.3057e-01
epoch 5200 loss = 9.3055e-01
epoch 5250 loss = 9.3053e-01
epoch 5300 loss = 9.3051e-01
epoch 5350 loss = 9.3049e-01
epoch 5400 loss = 9.3047e-01
epoch 5450 loss = 9.3045e-01
epoch 5500 loss = 9.3043e-01
epoch 5550 loss = 9.3041e-01
epoch 5600 loss = 9.304e-01
epoch 5650 loss = 9.3038e-01
epoch 5700 loss = 9.3037e-01
epoch 5750 loss = 9.3035e-01
epoch 5800 loss = 9.3034e-01
epoch 5850 loss = 9.3032e-01
epoch 5900 loss = 9.3031e-01
epoch 5950 loss = 9.3030e-01
epoch 6000 loss = 9.3029e-01
epoch 6050 loss = 9.3028e-01
epoch 6100 loss = 9.3027e-01
epoch 6150 loss = 9.3026e-01
epoch 6200 loss = 9.3025e-01
epoch 6250 loss = 9.3024e-01
epoch 6300 loss = 9.3023e-01
epoch 6320 loss = 9.3023e-01
*************** END FIRST PHASE ***************
* Training time: 16.629652976989746s
* Average epoch time: 0.0026312742052198965s
***************** END TRAINING ****************
* Training time: 16.629652976989746s
_ _ ____ ___ __ __
| \ | | ___ _ _| _ \ / _ \| \/ |
| \| |/ _ \ | | | |_) | | | | |\/| |
| |\ | __/ |_| | _ <| |_| | | | |
|_| \_|\___|\__,_|_| \_\ ___/|_| |_|
2024.09.18
************ MESH READING COMPLETE ************
* Dimension of the problem: 2D
* Elements type: t3: 3-node triangle
* Number of Dofs: 88
* Refinement level: 0
**************** START TRAINING ***************
epoch 2 loss = 9.4093e-01
epoch 3 loss = 9.3267e-01
epoch 4 loss = 9.3022e-01
epoch 5 loss = 9.3013e-01
epoch 6 loss = 9.3012e-01
epoch 7 loss = 9.3012e-01
epoch 8 loss = 9.3012e-01
*************** END FIRST PHASE ***************
* Training time: 0.22239208221435547s
* Average epoch time: 0.027799010276794434s
***************** END TRAINING ****************
* Training time: 0.22239208221435547s
_ _ ____ ___ __ __
| \ | | ___ _ _| _ \ / _ \| \/ |
| \| |/ _ \ | | | |_) | | | | |\/| |
| |\ | __/ |_| | _ <| |_| | | | |
|_| \_|\___|\__,_|_| \_\ ___/|_| |_|
2024.09.18
************ MESH READING COMPLETE ************
* Dimension of the problem: 2D
* Elements type: t3: 3-node triangle
* Number of Dofs: 88
* Refinement level: 0
**************** START TRAINING ***************
epoch 2 loss = 6.4619e+00
epoch 50 loss = 5.1704e+00
epoch 100 loss = 4.1221e+00
epoch 150 loss = 3.3192e+00
epoch 200 loss = 2.7077e+00
epoch 250 loss = 2.2451e+00
epoch 300 loss = 1.8976e+00
epoch 350 loss = 1.6385e+00
epoch 400 loss = 1.4464e+00
epoch 450 loss = 1.3049e+00
epoch 500 loss = 1.2011e+00
epoch 550 loss = 1.1253e+00
epoch 600 loss = 1.0702e+00
epoch 650 loss = 1.0304e+00
epoch 700 loss = 1.0018e+00
epoch 750 loss = 9.814e-01
epoch 800 loss = 9.6694e-01
epoch 850 loss = 9.5677e-01
epoch 900 loss = 9.4965e-01
epoch 950 loss = 9.447e-01
epoch 1000 loss = 9.4126e-01
epoch 1050 loss = 9.3889e-01
epoch 1100 loss = 9.3724e-01
epoch 1150 loss = 9.361e-01
epoch 1200 loss = 9.353e-01
epoch 1250 loss = 9.3473e-01
epoch 1300 loss = 9.3431e-01
epoch 1350 loss = 9.3401e-01
epoch 1400 loss = 9.3378e-01
epoch 1450 loss = 9.336e-01
epoch 1500 loss = 9.3345e-01
epoch 1550 loss = 9.3333e-01
epoch 1600 loss = 9.3323e-01
epoch 1650 loss = 9.3315e-01
epoch 1700 loss = 9.3308e-01
epoch 1750 loss = 9.3301e-01
epoch 1800 loss = 9.3295e-01
epoch 1850 loss = 9.329e-01
epoch 1900 loss = 9.3285e-01
epoch 1950 loss = 9.3281e-01
epoch 2000 loss = 9.3276e-01
epoch 2050 loss = 9.3273e-01
epoch 2100 loss = 9.3269e-01
epoch 2150 loss = 9.3265e-01
epoch 2200 loss = 9.3261e-01
epoch 2250 loss = 9.3258e-01
epoch 2300 loss = 9.3254e-01
epoch 2350 loss = 9.3251e-01
epoch 2400 loss = 9.3247e-01
epoch 2450 loss = 9.3244e-01
epoch 2500 loss = 9.3240e-01
epoch 2550 loss = 9.3237e-01
epoch 2600 loss = 9.3233e-01
epoch 2650 loss = 9.3229e-01
epoch 2700 loss = 9.3226e-01
epoch 2750 loss = 9.3222e-01
epoch 2800 loss = 9.3218e-01
epoch 2850 loss = 9.3215e-01
epoch 2900 loss = 9.3211e-01
epoch 2950 loss = 9.3207e-01
epoch 3000 loss = 9.3204e-01
epoch 3050 loss = 9.32e-01
epoch 3100 loss = 9.3196e-01
epoch 3150 loss = 9.3192e-01
epoch 3200 loss = 9.3188e-01
epoch 3250 loss = 9.3185e-01
epoch 3300 loss = 9.3181e-01
epoch 3350 loss = 9.3177e-01
epoch 3400 loss = 9.3173e-01
epoch 3450 loss = 9.3169e-01
epoch 3500 loss = 9.3165e-01
epoch 3550 loss = 9.3161e-01
epoch 3600 loss = 9.3158e-01
epoch 3650 loss = 9.3154e-01
epoch 3700 loss = 9.315e-01
epoch 3750 loss = 9.3146e-01
epoch 3800 loss = 9.3142e-01
epoch 3850 loss = 9.3139e-01
epoch 3900 loss = 9.3135e-01
epoch 3950 loss = 9.3131e-01
epoch 4000 loss = 9.3127e-01
epoch 4050 loss = 9.3124e-01
epoch 4100 loss = 9.3120e-01
epoch 4150 loss = 9.3117e-01
epoch 4200 loss = 9.3113e-01
epoch 4250 loss = 9.311e-01
epoch 4300 loss = 9.3106e-01
epoch 4350 loss = 9.3103e-01
epoch 4400 loss = 9.3099e-01
epoch 4450 loss = 9.3096e-01
epoch 4500 loss = 9.3093e-01
epoch 4550 loss = 9.309e-01
epoch 4600 loss = 9.3087e-01
epoch 4650 loss = 9.3084e-01
epoch 4700 loss = 9.3081e-01
epoch 4750 loss = 9.3078e-01
epoch 4800 loss = 9.3075e-01
epoch 4850 loss = 9.3072e-01
epoch 4900 loss = 9.3069e-01
epoch 4950 loss = 9.3067e-01
epoch 5000 loss = 9.3064e-01
epoch 5050 loss = 9.3062e-01
epoch 5100 loss = 9.3059e-01
epoch 5150 loss = 9.3057e-01
epoch 5200 loss = 9.3055e-01
epoch 5250 loss = 9.3053e-01
epoch 5300 loss = 9.3051e-01
epoch 5350 loss = 9.3049e-01
epoch 5400 loss = 9.3047e-01
epoch 5450 loss = 9.3045e-01
epoch 5500 loss = 9.3043e-01
epoch 5550 loss = 9.3041e-01
epoch 5600 loss = 9.304e-01
epoch 5650 loss = 9.3038e-01
epoch 5700 loss = 9.3037e-01
epoch 5750 loss = 9.3035e-01
epoch 5800 loss = 9.3034e-01
epoch 5850 loss = 9.3032e-01
epoch 5900 loss = 9.3031e-01
epoch 5950 loss = 9.3030e-01
epoch 6000 loss = 9.3029e-01
epoch 6050 loss = 9.3028e-01
epoch 6100 loss = 9.3027e-01
epoch 6150 loss = 9.3026e-01
epoch 6200 loss = 9.3025e-01
epoch 6250 loss = 9.3024e-01
epoch 6300 loss = 9.3023e-01
epoch 6320 loss = 9.3023e-01
*************** END FIRST PHASE ***************
* Training time: 16.67806601524353s
* Average epoch time: 0.002638934496082837s
************ MESH READING COMPLETE ************
* Dimension of the problem: 2D
* Elements type: t3: 3-node triangle
* Number of Dofs: 288
* Refinement level: 1
**************** START TRAINING ***************
epoch 2 loss = 8.9251e-01
epoch 50 loss = 8.7081e-01
epoch 100 loss = 8.6841e-01
epoch 150 loss = 8.6669e-01
epoch 200 loss = 8.6521e-01
epoch 250 loss = 8.6392e-01
epoch 300 loss = 8.6283e-01
epoch 350 loss = 8.6196e-01
epoch 400 loss = 8.6128e-01
epoch 450 loss = 8.6076e-01
epoch 500 loss = 8.6036e-01
epoch 550 loss = 8.6006e-01
epoch 600 loss = 8.5983e-01
epoch 650 loss = 8.5963e-01
epoch 700 loss = 8.5947e-01
epoch 750 loss = 8.5935e-01
epoch 800 loss = 8.5924e-01
epoch 850 loss = 8.5916e-01
epoch 900 loss = 8.5908e-01
epoch 950 loss = 8.5902e-01
epoch 1000 loss = 8.5897e-01
epoch 1050 loss = 8.5892e-01
epoch 1100 loss = 8.5888e-01
epoch 1150 loss = 8.5884e-01
epoch 1200 loss = 8.588e-01
epoch 1250 loss = 8.5876e-01
epoch 1300 loss = 8.5873e-01
epoch 1350 loss = 8.5870e-01
epoch 1400 loss = 8.5867e-01
epoch 1450 loss = 8.5865e-01
epoch 1458 loss = 8.5865e-01
*************** END FIRST PHASE ***************
* Training time: 4.004687070846558s
* Average epoch time: 0.0027466989511979135s
***************** END TRAINING ****************
* Training time: 20.682753086090088s
_ _ ____ ___ __ __
| \ | | ___ _ _| _ \ / _ \| \/ |
| \| |/ _ \ | | | |_) | | | | |\/| |
| |\ | __/ |_| | _ <| |_| | | | |
|_| \_|\___|\__,_|_| \_\ ___/|_| |_|
2024.09.18
************ MESH READING COMPLETE ************
* Dimension of the problem: 2D
* Elements type: t3: 3-node triangle
* Number of Dofs: 88
* Refinement level: 0
**************** START TRAINING ***************
epoch 2 loss = 9.4093e-01
epoch 3 loss = 9.3267e-01
epoch 4 loss = 9.3022e-01
epoch 5 loss = 9.3013e-01
epoch 6 loss = 9.3012e-01
epoch 7 loss = 9.3012e-01
epoch 8 loss = 9.3012e-01
*************** END FIRST PHASE ***************
* Training time: 0.22462201118469238s
* Average epoch time: 0.028077751398086548s
************ MESH READING COMPLETE ************
* Dimension of the problem: 2D
* Elements type: t3: 3-node triangle
* Number of Dofs: 288
* Refinement level: 1
**************** START TRAINING ***************
epoch 2 loss = 8.6992e-01
epoch 3 loss = 8.6361e-01
epoch 4 loss = 8.6262e-01
epoch 5 loss = 8.6182e-01
epoch 6 loss = 8.6080e-01
epoch 7 loss = 8.6019e-01
epoch 8 loss = 8.5992e-01
epoch 9 loss = 8.5959e-01
epoch 10 loss = 8.5934e-01
epoch 11 loss = 8.5925e-01
epoch 12 loss = 8.5913e-01
epoch 13 loss = 8.5906e-01
epoch 14 loss = 8.5902e-01
epoch 15 loss = 8.5899e-01
epoch 16 loss = 8.5895e-01
epoch 17 loss = 8.5894e-01
epoch 18 loss = 8.589e-01
epoch 19 loss = 8.5889e-01
epoch 20 loss = 8.5887e-01
epoch 21 loss = 8.5885e-01
epoch 22 loss = 8.5883e-01
epoch 23 loss = 8.5881e-01
epoch 24 loss = 8.5879e-01
epoch 25 loss = 8.5878e-01
epoch 26 loss = 8.5877e-01
epoch 27 loss = 8.5874e-01
epoch 28 loss = 8.5873e-01
epoch 29 loss = 8.5871e-01
epoch 30 loss = 8.5869e-01
epoch 31 loss = 8.5867e-01
epoch 32 loss = 8.5865e-01
epoch 33 loss = 8.5864e-01
epoch 34 loss = 8.5863e-01
epoch 35 loss = 8.5862e-01
epoch 36 loss = 8.5862e-01
epoch 37 loss = 8.5861e-01
epoch 38 loss = 8.5861e-01
epoch 39 loss = 8.5861e-01
epoch 40 loss = 8.5861e-01
epoch 41 loss = 8.5861e-01
epoch 42 loss = 8.5861e-01
epoch 43 loss = 8.5860e-01
epoch 44 loss = 8.5860e-01
epoch 45 loss = 8.5860e-01
epoch 46 loss = 8.5860e-01
epoch 47 loss = 8.5860e-01
epoch 48 loss = 8.5860e-01
epoch 49 loss = 8.5860e-01
epoch 50 loss = 8.5860e-01
epoch 51 loss = 8.5860e-01
epoch 52 loss = 8.5860e-01
epoch 53 loss = 8.5860e-01
epoch 54 loss = 8.586e-01
epoch 55 loss = 8.586e-01
epoch 56 loss = 8.586e-01
epoch 57 loss = 8.586e-01
epoch 58 loss = 8.586e-01
epoch 59 loss = 8.586e-01
epoch 60 loss = 8.586e-01
epoch 61 loss = 8.586e-01
epoch 62 loss = 8.586e-01
epoch 63 loss = 8.586e-01
epoch 64 loss = 8.586e-01
epoch 65 loss = 8.586e-01
epoch 66 loss = 8.586e-01
epoch 67 loss = 8.586e-01
epoch 68 loss = 8.586e-01
epoch 69 loss = 8.586e-01
epoch 70 loss = 8.586e-01
epoch 71 loss = 8.5859e-01
epoch 72 loss = 8.5859e-01
epoch 73 loss = 8.5859e-01
epoch 74 loss = 8.5859e-01
epoch 75 loss = 8.5859e-01
epoch 76 loss = 8.5859e-01
epoch 77 loss = 8.5859e-01
epoch 78 loss = 8.5859e-01
epoch 79 loss = 8.5859e-01
epoch 80 loss = 8.5858e-01
epoch 81 loss = 8.5858e-01
epoch 82 loss = 8.5858e-01
epoch 83 loss = 8.5858e-01
epoch 84 loss = 8.5858e-01
epoch 85 loss = 8.5858e-01
epoch 86 loss = 8.5858e-01
epoch 87 loss = 8.5858e-01
epoch 88 loss = 8.5858e-01
epoch 89 loss = 8.5858e-01
epoch 90 loss = 8.5858e-01
epoch 91 loss = 8.5858e-01
epoch 92 loss = 8.5858e-01
epoch 93 loss = 8.5858e-01
epoch 94 loss = 8.5858e-01
epoch 95 loss = 8.5858e-01
epoch 96 loss = 8.5858e-01
epoch 97 loss = 8.5858e-01
*************** END FIRST PHASE ***************
* Training time: 4.283379316329956s
* Average epoch time: 0.044158549652886146s
***************** END TRAINING ****************
* Training time: 4.508001327514648s
_ _ ____ ___ __ __
| \ | | ___ _ _| _ \ / _ \| \/ |
| \| |/ _ \ | | | |_) | | | | |\/| |
| |\ | __/ |_| | _ <| |_| | | | |
|_| \_|\___|\__,_|_| \_\ ___/|_| |_|
2024.09.18
************ MESH READING COMPLETE ************
* Dimension of the problem: 2D
* Elements type: t3: 3-node triangle
* Number of Dofs: 88
* Refinement level: 0
**************** START TRAINING ***************
epoch 2 loss = 6.4619e+00
epoch 50 loss = 5.1704e+00
epoch 100 loss = 4.1221e+00
epoch 150 loss = 3.3192e+00
epoch 200 loss = 2.7077e+00
epoch 250 loss = 2.2451e+00
epoch 300 loss = 1.8976e+00
epoch 350 loss = 1.6385e+00
epoch 400 loss = 1.4464e+00
epoch 450 loss = 1.3049e+00
epoch 500 loss = 1.2011e+00
epoch 550 loss = 1.1253e+00
epoch 600 loss = 1.0702e+00
epoch 650 loss = 1.0304e+00
epoch 700 loss = 1.0018e+00
epoch 750 loss = 9.814e-01
epoch 800 loss = 9.6694e-01
epoch 850 loss = 9.5677e-01
epoch 900 loss = 9.4965e-01
epoch 950 loss = 9.447e-01
epoch 1000 loss = 9.4126e-01
epoch 1050 loss = 9.3889e-01
epoch 1100 loss = 9.3724e-01
epoch 1150 loss = 9.361e-01
epoch 1200 loss = 9.353e-01
epoch 1250 loss = 9.3473e-01
epoch 1300 loss = 9.3431e-01
epoch 1350 loss = 9.3401e-01
epoch 1400 loss = 9.3378e-01
epoch 1450 loss = 9.336e-01
epoch 1500 loss = 9.3345e-01
epoch 1550 loss = 9.3333e-01
epoch 1600 loss = 9.3323e-01
epoch 1650 loss = 9.3315e-01
epoch 1700 loss = 9.3308e-01
epoch 1750 loss = 9.3301e-01
epoch 1800 loss = 9.3295e-01
epoch 1850 loss = 9.329e-01
epoch 1900 loss = 9.3285e-01
epoch 1950 loss = 9.3281e-01
epoch 2000 loss = 9.3276e-01
epoch 2050 loss = 9.3273e-01
epoch 2100 loss = 9.3269e-01
epoch 2150 loss = 9.3265e-01
epoch 2200 loss = 9.3261e-01
epoch 2250 loss = 9.3258e-01
epoch 2300 loss = 9.3254e-01
epoch 2350 loss = 9.3251e-01
epoch 2400 loss = 9.3247e-01
epoch 2450 loss = 9.3244e-01
epoch 2500 loss = 9.3240e-01
epoch 2550 loss = 9.3237e-01
epoch 2600 loss = 9.3233e-01
epoch 2650 loss = 9.3229e-01
epoch 2700 loss = 9.3226e-01
epoch 2750 loss = 9.3222e-01
epoch 2800 loss = 9.3218e-01
epoch 2850 loss = 9.3215e-01
epoch 2900 loss = 9.3211e-01
epoch 2950 loss = 9.3207e-01
epoch 3000 loss = 9.3204e-01
epoch 3050 loss = 9.32e-01
epoch 3100 loss = 9.3196e-01
epoch 3150 loss = 9.3192e-01
epoch 3200 loss = 9.3188e-01
epoch 3250 loss = 9.3185e-01
epoch 3300 loss = 9.3181e-01
epoch 3350 loss = 9.3177e-01
epoch 3400 loss = 9.3173e-01
epoch 3450 loss = 9.3169e-01
epoch 3500 loss = 9.3165e-01
epoch 3550 loss = 9.3161e-01
epoch 3600 loss = 9.3158e-01
epoch 3650 loss = 9.3154e-01
epoch 3700 loss = 9.315e-01
epoch 3750 loss = 9.3146e-01
epoch 3800 loss = 9.3142e-01
epoch 3850 loss = 9.3139e-01
epoch 3900 loss = 9.3135e-01
epoch 3950 loss = 9.3131e-01
epoch 4000 loss = 9.3127e-01
epoch 4050 loss = 9.3124e-01
epoch 4100 loss = 9.3120e-01
epoch 4150 loss = 9.3117e-01
epoch 4200 loss = 9.3113e-01
epoch 4250 loss = 9.311e-01
epoch 4300 loss = 9.3106e-01
epoch 4350 loss = 9.3103e-01
epoch 4400 loss = 9.3099e-01
epoch 4450 loss = 9.3096e-01
epoch 4500 loss = 9.3093e-01
epoch 4550 loss = 9.309e-01
epoch 4600 loss = 9.3087e-01
epoch 4650 loss = 9.3084e-01
epoch 4700 loss = 9.3081e-01
epoch 4750 loss = 9.3078e-01
epoch 4800 loss = 9.3075e-01
epoch 4850 loss = 9.3072e-01
epoch 4900 loss = 9.3069e-01
epoch 4950 loss = 9.3067e-01
epoch 5000 loss = 9.3064e-01
epoch 5050 loss = 9.3062e-01
epoch 5100 loss = 9.3059e-01
epoch 5150 loss = 9.3057e-01
epoch 5200 loss = 9.3055e-01
epoch 5250 loss = 9.3053e-01
epoch 5300 loss = 9.3051e-01
epoch 5350 loss = 9.3049e-01
epoch 5400 loss = 9.3047e-01
epoch 5450 loss = 9.3045e-01
epoch 5500 loss = 9.3043e-01
epoch 5550 loss = 9.3041e-01
epoch 5600 loss = 9.304e-01
epoch 5650 loss = 9.3038e-01
epoch 5700 loss = 9.3037e-01
epoch 5750 loss = 9.3035e-01
epoch 5800 loss = 9.3034e-01
epoch 5850 loss = 9.3032e-01
epoch 5900 loss = 9.3031e-01
epoch 5950 loss = 9.3030e-01
epoch 6000 loss = 9.3029e-01
epoch 6050 loss = 9.3028e-01
epoch 6100 loss = 9.3027e-01
epoch 6150 loss = 9.3026e-01
epoch 6200 loss = 9.3025e-01
epoch 6250 loss = 9.3024e-01
epoch 6300 loss = 9.3023e-01
epoch 6320 loss = 9.3023e-01
*************** END FIRST PHASE ***************
* Training time: 16.69234299659729s
* Average epoch time: 0.0026411935121198244s
************ MESH READING COMPLETE ************
* Dimension of the problem: 2D
* Elements type: t3: 3-node triangle
* Number of Dofs: 288
* Refinement level: 1
**************** START TRAINING ***************
epoch 2 loss = 8.9251e-01
epoch 50 loss = 8.7081e-01
epoch 100 loss = 8.6841e-01
epoch 150 loss = 8.6669e-01
epoch 200 loss = 8.6521e-01
epoch 250 loss = 8.6392e-01
epoch 300 loss = 8.6283e-01
epoch 350 loss = 8.6196e-01
epoch 400 loss = 8.6128e-01
epoch 450 loss = 8.6076e-01
epoch 500 loss = 8.6036e-01
epoch 550 loss = 8.6006e-01
epoch 600 loss = 8.5983e-01
epoch 650 loss = 8.5963e-01
epoch 700 loss = 8.5947e-01
epoch 750 loss = 8.5935e-01
epoch 800 loss = 8.5924e-01
epoch 850 loss = 8.5916e-01
epoch 900 loss = 8.5908e-01
epoch 950 loss = 8.5902e-01
epoch 1000 loss = 8.5897e-01
epoch 1050 loss = 8.5892e-01
epoch 1100 loss = 8.5888e-01
epoch 1150 loss = 8.5884e-01
epoch 1200 loss = 8.588e-01
epoch 1250 loss = 8.5876e-01
epoch 1300 loss = 8.5873e-01
epoch 1350 loss = 8.5870e-01
epoch 1400 loss = 8.5867e-01
epoch 1450 loss = 8.5865e-01
epoch 1458 loss = 8.5865e-01
*************** END FIRST PHASE ***************
* Training time: 4.026496887207031s
* Average epoch time: 0.002761657672981503s
************ MESH READING COMPLETE ************
* Dimension of the problem: 2D
* Elements type: t3: 3-node triangle
* Number of Dofs: 968
* Refinement level: 2
**************** START TRAINING ***************
epoch 2 loss = 8.4782e-01
epoch 50 loss = 8.4136e-01
epoch 100 loss = 8.4051e-01
epoch 150 loss = 8.3992e-01
epoch 200 loss = 8.3949e-01
epoch 250 loss = 8.3919e-01
epoch 300 loss = 8.3897e-01
epoch 350 loss = 8.3879e-01
epoch 400 loss = 8.3865e-01
epoch 450 loss = 8.3851e-01
epoch 500 loss = 8.3839e-01
epoch 550 loss = 8.3827e-01
epoch 600 loss = 8.3817e-01
epoch 650 loss = 8.3809e-01
epoch 700 loss = 8.3802e-01
epoch 750 loss = 8.3796e-01
epoch 800 loss = 8.3791e-01
epoch 833 loss = 8.3789e-01
*************** END FIRST PHASE ***************
* Training time: 2.735910177230835s
* Average epoch time: 0.0032844059750670286s
***************** END TRAINING ****************
* Training time: 23.454750061035156s
_ _ ____ ___ __ __
| \ | | ___ _ _| _ \ / _ \| \/ |
| \| |/ _ \ | | | |_) | | | | |\/| |
| |\ | __/ |_| | _ <| |_| | | | |
|_| \_|\___|\__,_|_| \_\ ___/|_| |_|
2024.09.18
************ MESH READING COMPLETE ************
* Dimension of the problem: 2D
* Elements type: t3: 3-node triangle
* Number of Dofs: 88
* Refinement level: 0
**************** START TRAINING ***************
epoch 2 loss = 9.4093e-01
epoch 3 loss = 9.3267e-01
epoch 4 loss = 9.3022e-01
epoch 5 loss = 9.3013e-01
epoch 6 loss = 9.3012e-01
epoch 7 loss = 9.3012e-01
epoch 8 loss = 9.3012e-01
*************** END FIRST PHASE ***************
* Training time: 0.2236180305480957s
* Average epoch time: 0.027952253818511963s
************ MESH READING COMPLETE ************
* Dimension of the problem: 2D
* Elements type: t3: 3-node triangle
* Number of Dofs: 288
* Refinement level: 1
**************** START TRAINING ***************
epoch 2 loss = 8.6992e-01
epoch 3 loss = 8.6361e-01
epoch 4 loss = 8.6262e-01
epoch 5 loss = 8.6182e-01
epoch 6 loss = 8.6080e-01
epoch 7 loss = 8.6019e-01
epoch 8 loss = 8.5992e-01
epoch 9 loss = 8.5959e-01
epoch 10 loss = 8.5934e-01
epoch 11 loss = 8.5925e-01
epoch 12 loss = 8.5913e-01
epoch 13 loss = 8.5906e-01
epoch 14 loss = 8.5902e-01
epoch 15 loss = 8.5899e-01
epoch 16 loss = 8.5895e-01
epoch 17 loss = 8.5894e-01
epoch 18 loss = 8.589e-01
epoch 19 loss = 8.5889e-01
epoch 20 loss = 8.5887e-01
epoch 21 loss = 8.5885e-01
epoch 22 loss = 8.5883e-01
epoch 23 loss = 8.5881e-01
epoch 24 loss = 8.5879e-01
epoch 25 loss = 8.5878e-01
epoch 26 loss = 8.5877e-01
epoch 27 loss = 8.5874e-01
epoch 28 loss = 8.5873e-01
epoch 29 loss = 8.5871e-01
epoch 30 loss = 8.5869e-01
epoch 31 loss = 8.5867e-01
epoch 32 loss = 8.5865e-01
epoch 33 loss = 8.5864e-01
epoch 34 loss = 8.5863e-01
epoch 35 loss = 8.5862e-01
epoch 36 loss = 8.5862e-01
epoch 37 loss = 8.5861e-01
epoch 38 loss = 8.5861e-01
epoch 39 loss = 8.5861e-01
epoch 40 loss = 8.5861e-01
epoch 41 loss = 8.5861e-01
epoch 42 loss = 8.5861e-01
epoch 43 loss = 8.5860e-01
epoch 44 loss = 8.5860e-01
epoch 45 loss = 8.5860e-01
epoch 46 loss = 8.5860e-01
epoch 47 loss = 8.5860e-01
epoch 48 loss = 8.5860e-01
epoch 49 loss = 8.5860e-01
epoch 50 loss = 8.5860e-01
epoch 51 loss = 8.5860e-01
epoch 52 loss = 8.5860e-01
epoch 53 loss = 8.5860e-01
epoch 54 loss = 8.586e-01
epoch 55 loss = 8.586e-01
epoch 56 loss = 8.586e-01
epoch 57 loss = 8.586e-01
epoch 58 loss = 8.586e-01
epoch 59 loss = 8.586e-01
epoch 60 loss = 8.586e-01
epoch 61 loss = 8.586e-01
epoch 62 loss = 8.586e-01
epoch 63 loss = 8.586e-01
epoch 64 loss = 8.586e-01
epoch 65 loss = 8.586e-01
epoch 66 loss = 8.586e-01
epoch 67 loss = 8.586e-01
epoch 68 loss = 8.586e-01
epoch 69 loss = 8.586e-01
epoch 70 loss = 8.586e-01
epoch 71 loss = 8.5859e-01
epoch 72 loss = 8.5859e-01
epoch 73 loss = 8.5859e-01
epoch 74 loss = 8.5859e-01
epoch 75 loss = 8.5859e-01
epoch 76 loss = 8.5859e-01
epoch 77 loss = 8.5859e-01
epoch 78 loss = 8.5859e-01
epoch 79 loss = 8.5859e-01
epoch 80 loss = 8.5858e-01
epoch 81 loss = 8.5858e-01
epoch 82 loss = 8.5858e-01
epoch 83 loss = 8.5858e-01
epoch 84 loss = 8.5858e-01
epoch 85 loss = 8.5858e-01
epoch 86 loss = 8.5858e-01
epoch 87 loss = 8.5858e-01
epoch 88 loss = 8.5858e-01
epoch 89 loss = 8.5858e-01
epoch 90 loss = 8.5858e-01
epoch 91 loss = 8.5858e-01
epoch 92 loss = 8.5858e-01
epoch 93 loss = 8.5858e-01
epoch 94 loss = 8.5858e-01
epoch 95 loss = 8.5858e-01
epoch 96 loss = 8.5858e-01
epoch 97 loss = 8.5858e-01
*************** END FIRST PHASE ***************
* Training time: 4.3030009269714355s
* Average epoch time: 0.044360834298674594s
************ MESH READING COMPLETE ************
* Dimension of the problem: 2D
* Elements type: t3: 3-node triangle
* Number of Dofs: 968
* Refinement level: 2
**************** START TRAINING ***************
epoch 2 loss = 8.4175e-01
epoch 3 loss = 8.4083e-01
epoch 4 loss = 8.4027e-01
epoch 5 loss = 8.3996e-01
epoch 6 loss = 8.3969e-01
epoch 7 loss = 8.395e-01
epoch 8 loss = 8.3930e-01
epoch 9 loss = 8.3913e-01
epoch 10 loss = 8.3893e-01
epoch 11 loss = 8.3875e-01
epoch 12 loss = 8.3866e-01
epoch 13 loss = 8.3856e-01
epoch 14 loss = 8.3848e-01
epoch 15 loss = 8.3839e-01
epoch 16 loss = 8.3835e-01
epoch 17 loss = 8.3827e-01
epoch 18 loss = 8.3824e-01
epoch 19 loss = 8.382e-01
epoch 20 loss = 8.3816e-01
epoch 21 loss = 8.3813e-01
epoch 22 loss = 8.3810e-01
epoch 23 loss = 8.3807e-01
epoch 24 loss = 8.3804e-01
epoch 25 loss = 8.3802e-01
epoch 26 loss = 8.38e-01
epoch 27 loss = 8.3797e-01
epoch 28 loss = 8.3795e-01
epoch 29 loss = 8.3793e-01
epoch 30 loss = 8.3792e-01
epoch 31 loss = 8.3790e-01
epoch 32 loss = 8.3789e-01
epoch 33 loss = 8.3788e-01
epoch 34 loss = 8.3785e-01
epoch 35 loss = 8.3784e-01
epoch 36 loss = 8.3782e-01
epoch 37 loss = 8.3781e-01
epoch 38 loss = 8.3779e-01
epoch 39 loss = 8.3778e-01
epoch 40 loss = 8.3776e-01
epoch 41 loss = 8.3775e-01
epoch 42 loss = 8.3774e-01
epoch 43 loss = 8.3773e-01
epoch 44 loss = 8.3772e-01
epoch 45 loss = 8.3771e-01
epoch 46 loss = 8.3770e-01
epoch 47 loss = 8.3769e-01
epoch 48 loss = 8.3769e-01
epoch 49 loss = 8.3767e-01
epoch 50 loss = 8.3767e-01
epoch 51 loss = 8.3766e-01
epoch 52 loss = 8.3765e-01
epoch 53 loss = 8.3765e-01
epoch 54 loss = 8.3764e-01
epoch 55 loss = 8.3763e-01
epoch 56 loss = 8.3763e-01
epoch 57 loss = 8.3762e-01
epoch 58 loss = 8.3762e-01
epoch 59 loss = 8.3761e-01
epoch 60 loss = 8.3760e-01
epoch 61 loss = 8.376e-01
epoch 62 loss = 8.3759e-01
epoch 63 loss = 8.3759e-01
epoch 64 loss = 8.3759e-01
epoch 65 loss = 8.3758e-01
epoch 66 loss = 8.3758e-01
epoch 67 loss = 8.3757e-01
epoch 68 loss = 8.3757e-01
epoch 69 loss = 8.3756e-01
epoch 70 loss = 8.3756e-01
epoch 71 loss = 8.3755e-01
epoch 72 loss = 8.3755e-01
epoch 73 loss = 8.3754e-01
epoch 74 loss = 8.3754e-01
epoch 75 loss = 8.3753e-01
epoch 76 loss = 8.3753e-01
epoch 77 loss = 8.3753e-01
epoch 78 loss = 8.3752e-01
epoch 79 loss = 8.3752e-01
epoch 80 loss = 8.3752e-01
epoch 81 loss = 8.3751e-01
epoch 82 loss = 8.3751e-01
epoch 83 loss = 8.3751e-01
epoch 84 loss = 8.3751e-01
epoch 85 loss = 8.3751e-01
*************** END FIRST PHASE ***************
* Training time: 4.473391056060791s
* Average epoch time: 0.052628130071303424s
***************** END TRAINING ****************
* Training time: 9.000010013580322s
_ _ ____ ___ __ __
| \ | | ___ _ _| _ \ / _ \| \/ |
| \| |/ _ \ | | | |_) | | | | |\/| |
| |\ | __/ |_| | _ <| |_| | | | |
|_| \_|\___|\__,_|_| \_\ ___/|_| |_|
2024.09.18
************ MESH READING COMPLETE ************
* Dimension of the problem: 2D
* Elements type: t3: 3-node triangle
* Number of Dofs: 88
* Refinement level: 0
**************** START TRAINING ***************
epoch 2 loss = 6.4619e+00
epoch 50 loss = 5.1704e+00
epoch 100 loss = 4.1221e+00
epoch 150 loss = 3.3192e+00
epoch 200 loss = 2.7077e+00
epoch 250 loss = 2.2451e+00
epoch 300 loss = 1.8976e+00
epoch 350 loss = 1.6385e+00
epoch 400 loss = 1.4464e+00
epoch 450 loss = 1.3049e+00
epoch 500 loss = 1.2011e+00
epoch 550 loss = 1.1253e+00
epoch 600 loss = 1.0702e+00
epoch 650 loss = 1.0304e+00
epoch 700 loss = 1.0018e+00
epoch 750 loss = 9.814e-01
epoch 800 loss = 9.6694e-01
epoch 850 loss = 9.5677e-01
epoch 900 loss = 9.4965e-01
epoch 950 loss = 9.447e-01
epoch 1000 loss = 9.4126e-01
epoch 1050 loss = 9.3889e-01
epoch 1100 loss = 9.3724e-01
epoch 1150 loss = 9.361e-01
epoch 1200 loss = 9.353e-01
epoch 1250 loss = 9.3473e-01
epoch 1300 loss = 9.3431e-01
epoch 1350 loss = 9.3401e-01
epoch 1400 loss = 9.3378e-01
epoch 1450 loss = 9.336e-01
epoch 1500 loss = 9.3345e-01
epoch 1550 loss = 9.3333e-01
epoch 1600 loss = 9.3323e-01
epoch 1650 loss = 9.3315e-01
epoch 1700 loss = 9.3308e-01
epoch 1750 loss = 9.3301e-01
epoch 1800 loss = 9.3295e-01
epoch 1850 loss = 9.329e-01
epoch 1900 loss = 9.3285e-01
epoch 1950 loss = 9.3281e-01
epoch 2000 loss = 9.3276e-01
epoch 2050 loss = 9.3273e-01
epoch 2100 loss = 9.3269e-01
epoch 2150 loss = 9.3265e-01
epoch 2200 loss = 9.3261e-01
epoch 2250 loss = 9.3258e-01
epoch 2300 loss = 9.3254e-01
epoch 2350 loss = 9.3251e-01
epoch 2400 loss = 9.3247e-01
epoch 2450 loss = 9.3244e-01
epoch 2500 loss = 9.3240e-01
epoch 2550 loss = 9.3237e-01
epoch 2600 loss = 9.3233e-01
epoch 2650 loss = 9.3229e-01
epoch 2700 loss = 9.3226e-01
epoch 2750 loss = 9.3222e-01
epoch 2800 loss = 9.3218e-01
epoch 2850 loss = 9.3215e-01
epoch 2900 loss = 9.3211e-01
epoch 2950 loss = 9.3207e-01
epoch 3000 loss = 9.3204e-01
epoch 3050 loss = 9.32e-01
epoch 3100 loss = 9.3196e-01
epoch 3150 loss = 9.3192e-01
epoch 3200 loss = 9.3188e-01
epoch 3250 loss = 9.3185e-01
epoch 3300 loss = 9.3181e-01
epoch 3350 loss = 9.3177e-01
epoch 3400 loss = 9.3173e-01
epoch 3450 loss = 9.3169e-01
epoch 3500 loss = 9.3165e-01
epoch 3550 loss = 9.3161e-01
epoch 3600 loss = 9.3158e-01
epoch 3650 loss = 9.3154e-01
epoch 3700 loss = 9.315e-01
epoch 3750 loss = 9.3146e-01
epoch 3800 loss = 9.3142e-01
epoch 3850 loss = 9.3139e-01
epoch 3900 loss = 9.3135e-01
epoch 3950 loss = 9.3131e-01
epoch 4000 loss = 9.3127e-01
epoch 4050 loss = 9.3124e-01
epoch 4100 loss = 9.3120e-01
epoch 4150 loss = 9.3117e-01
epoch 4200 loss = 9.3113e-01
epoch 4250 loss = 9.311e-01
epoch 4300 loss = 9.3106e-01
epoch 4350 loss = 9.3103e-01
epoch 4400 loss = 9.3099e-01
epoch 4450 loss = 9.3096e-01
epoch 4500 loss = 9.3093e-01
epoch 4550 loss = 9.309e-01
epoch 4600 loss = 9.3087e-01
epoch 4650 loss = 9.3084e-01
epoch 4700 loss = 9.3081e-01
epoch 4750 loss = 9.3078e-01
epoch 4800 loss = 9.3075e-01
epoch 4850 loss = 9.3072e-01
epoch 4900 loss = 9.3069e-01
epoch 4950 loss = 9.3067e-01
epoch 5000 loss = 9.3064e-01
epoch 5050 loss = 9.3062e-01
epoch 5100 loss = 9.3059e-01
epoch 5150 loss = 9.3057e-01
epoch 5200 loss = 9.3055e-01
epoch 5250 loss = 9.3053e-01
epoch 5300 loss = 9.3051e-01
epoch 5350 loss = 9.3049e-01
epoch 5400 loss = 9.3047e-01
epoch 5450 loss = 9.3045e-01
epoch 5500 loss = 9.3043e-01
epoch 5550 loss = 9.3041e-01
epoch 5600 loss = 9.304e-01
epoch 5650 loss = 9.3038e-01
epoch 5700 loss = 9.3037e-01
epoch 5750 loss = 9.3035e-01
epoch 5800 loss = 9.3034e-01
epoch 5850 loss = 9.3032e-01
epoch 5900 loss = 9.3031e-01
epoch 5950 loss = 9.3030e-01
epoch 6000 loss = 9.3029e-01
epoch 6050 loss = 9.3028e-01
epoch 6100 loss = 9.3027e-01
epoch 6150 loss = 9.3026e-01
epoch 6200 loss = 9.3025e-01
epoch 6250 loss = 9.3024e-01
epoch 6300 loss = 9.3023e-01
epoch 6320 loss = 9.3023e-01
*************** END FIRST PHASE ***************
* Training time: 16.702078104019165s
* Average epoch time: 0.0026427338772182225s
************ MESH READING COMPLETE ************
* Dimension of the problem: 2D
* Elements type: t3: 3-node triangle
* Number of Dofs: 288
* Refinement level: 1
**************** START TRAINING ***************
epoch 2 loss = 8.9251e-01
epoch 50 loss = 8.7081e-01
epoch 100 loss = 8.6841e-01
epoch 150 loss = 8.6669e-01
epoch 200 loss = 8.6521e-01
epoch 250 loss = 8.6392e-01
epoch 300 loss = 8.6283e-01
epoch 350 loss = 8.6196e-01
epoch 400 loss = 8.6128e-01
epoch 450 loss = 8.6076e-01
epoch 500 loss = 8.6036e-01
epoch 550 loss = 8.6006e-01
epoch 600 loss = 8.5983e-01
epoch 650 loss = 8.5963e-01
epoch 700 loss = 8.5947e-01
epoch 750 loss = 8.5935e-01
epoch 800 loss = 8.5924e-01
epoch 850 loss = 8.5916e-01
epoch 900 loss = 8.5908e-01
epoch 950 loss = 8.5902e-01
epoch 1000 loss = 8.5897e-01
epoch 1050 loss = 8.5892e-01
epoch 1100 loss = 8.5888e-01
epoch 1150 loss = 8.5884e-01
epoch 1200 loss = 8.588e-01
epoch 1250 loss = 8.5876e-01
epoch 1300 loss = 8.5873e-01
epoch 1350 loss = 8.5870e-01
epoch 1400 loss = 8.5867e-01
epoch 1450 loss = 8.5865e-01
epoch 1458 loss = 8.5865e-01
*************** END FIRST PHASE ***************
* Training time: 3.992384195327759s
* Average epoch time: 0.002738260764971028s
************ MESH READING COMPLETE ************
* Dimension of the problem: 2D
* Elements type: t3: 3-node triangle
* Number of Dofs: 968
* Refinement level: 2
**************** START TRAINING ***************
epoch 2 loss = 8.4782e-01
epoch 50 loss = 8.4136e-01
epoch 100 loss = 8.4051e-01
epoch 150 loss = 8.3992e-01
epoch 200 loss = 8.3949e-01
epoch 250 loss = 8.3919e-01
epoch 300 loss = 8.3897e-01
epoch 350 loss = 8.3879e-01
epoch 400 loss = 8.3865e-01
epoch 450 loss = 8.3851e-01
epoch 500 loss = 8.3839e-01
epoch 550 loss = 8.3827e-01
epoch 600 loss = 8.3817e-01
epoch 650 loss = 8.3809e-01
epoch 700 loss = 8.3802e-01
epoch 750 loss = 8.3796e-01
epoch 800 loss = 8.3791e-01
epoch 833 loss = 8.3789e-01
*************** END FIRST PHASE ***************
* Training time: 2.7125890254974365s
* Average epoch time: 0.0032564093943546657s
************ MESH READING COMPLETE ************
* Dimension of the problem: 2D
* Elements type: t3: 3-node triangle
* Number of Dofs: 3608
* Refinement level: 3
**************** START TRAINING ***************
epoch 2 loss = 8.435e-01
epoch 50 loss = 8.3246e-01
epoch 100 loss = 8.3225e-01
epoch 150 loss = 8.3212e-01
epoch 200 loss = 8.3204e-01
epoch 250 loss = 8.3197e-01
epoch 300 loss = 8.3192e-01
epoch 330 loss = 8.319e-01
*************** END FIRST PHASE ***************
* Training time: 1.9740009307861328s
* Average epoch time: 0.00598182100238222s
***************** END TRAINING ****************
* Training time: 25.381052255630493s
_ _ ____ ___ __ __
| \ | | ___ _ _| _ \ / _ \| \/ |
| \| |/ _ \ | | | |_) | | | | |\/| |
| |\ | __/ |_| | _ <| |_| | | | |
|_| \_|\___|\__,_|_| \_\ ___/|_| |_|
2024.09.18
************ MESH READING COMPLETE ************
* Dimension of the problem: 2D
* Elements type: t3: 3-node triangle
* Number of Dofs: 88
* Refinement level: 0
**************** START TRAINING ***************
epoch 2 loss = 9.4093e-01
epoch 3 loss = 9.3267e-01
epoch 4 loss = 9.3022e-01
epoch 5 loss = 9.3013e-01
epoch 6 loss = 9.3012e-01
epoch 7 loss = 9.3012e-01
epoch 8 loss = 9.3012e-01
*************** END FIRST PHASE ***************
* Training time: 0.22039413452148438s
* Average epoch time: 0.027549266815185547s
************ MESH READING COMPLETE ************
* Dimension of the problem: 2D
* Elements type: t3: 3-node triangle
* Number of Dofs: 288
* Refinement level: 1
**************** START TRAINING ***************
epoch 2 loss = 8.6992e-01
epoch 3 loss = 8.6361e-01
epoch 4 loss = 8.6262e-01
epoch 5 loss = 8.6182e-01
epoch 6 loss = 8.6080e-01
epoch 7 loss = 8.6019e-01
epoch 8 loss = 8.5992e-01
epoch 9 loss = 8.5959e-01
epoch 10 loss = 8.5934e-01
epoch 11 loss = 8.5925e-01
epoch 12 loss = 8.5913e-01
epoch 13 loss = 8.5906e-01
epoch 14 loss = 8.5902e-01
epoch 15 loss = 8.5899e-01
epoch 16 loss = 8.5895e-01
epoch 17 loss = 8.5894e-01
epoch 18 loss = 8.589e-01
epoch 19 loss = 8.5889e-01
epoch 20 loss = 8.5887e-01
epoch 21 loss = 8.5885e-01
epoch 22 loss = 8.5883e-01
epoch 23 loss = 8.5881e-01
epoch 24 loss = 8.5879e-01
epoch 25 loss = 8.5878e-01
epoch 26 loss = 8.5877e-01
epoch 27 loss = 8.5874e-01
epoch 28 loss = 8.5873e-01
epoch 29 loss = 8.5871e-01
epoch 30 loss = 8.5869e-01
epoch 31 loss = 8.5867e-01
epoch 32 loss = 8.5865e-01
epoch 33 loss = 8.5864e-01
epoch 34 loss = 8.5863e-01
epoch 35 loss = 8.5862e-01
epoch 36 loss = 8.5862e-01
epoch 37 loss = 8.5861e-01
epoch 38 loss = 8.5861e-01
epoch 39 loss = 8.5861e-01
epoch 40 loss = 8.5861e-01
epoch 41 loss = 8.5861e-01
epoch 42 loss = 8.5861e-01
epoch 43 loss = 8.5860e-01
epoch 44 loss = 8.5860e-01
epoch 45 loss = 8.5860e-01
epoch 46 loss = 8.5860e-01
epoch 47 loss = 8.5860e-01
epoch 48 loss = 8.5860e-01
epoch 49 loss = 8.5860e-01
epoch 50 loss = 8.5860e-01
epoch 51 loss = 8.5860e-01
epoch 52 loss = 8.5860e-01
epoch 53 loss = 8.5860e-01
epoch 54 loss = 8.586e-01
epoch 55 loss = 8.586e-01
epoch 56 loss = 8.586e-01
epoch 57 loss = 8.586e-01
epoch 58 loss = 8.586e-01
epoch 59 loss = 8.586e-01
epoch 60 loss = 8.586e-01
epoch 61 loss = 8.586e-01
epoch 62 loss = 8.586e-01
epoch 63 loss = 8.586e-01
epoch 64 loss = 8.586e-01
epoch 65 loss = 8.586e-01
epoch 66 loss = 8.586e-01
epoch 67 loss = 8.586e-01
epoch 68 loss = 8.586e-01
epoch 69 loss = 8.586e-01
epoch 70 loss = 8.586e-01
epoch 71 loss = 8.5859e-01
epoch 72 loss = 8.5859e-01
epoch 73 loss = 8.5859e-01
epoch 74 loss = 8.5859e-01
epoch 75 loss = 8.5859e-01
epoch 76 loss = 8.5859e-01
epoch 77 loss = 8.5859e-01
epoch 78 loss = 8.5859e-01
epoch 79 loss = 8.5859e-01
epoch 80 loss = 8.5858e-01
epoch 81 loss = 8.5858e-01
epoch 82 loss = 8.5858e-01
epoch 83 loss = 8.5858e-01
epoch 84 loss = 8.5858e-01
epoch 85 loss = 8.5858e-01
epoch 86 loss = 8.5858e-01
epoch 87 loss = 8.5858e-01
epoch 88 loss = 8.5858e-01
epoch 89 loss = 8.5858e-01
epoch 90 loss = 8.5858e-01
epoch 91 loss = 8.5858e-01
epoch 92 loss = 8.5858e-01
epoch 93 loss = 8.5858e-01
epoch 94 loss = 8.5858e-01
epoch 95 loss = 8.5858e-01
epoch 96 loss = 8.5858e-01
epoch 97 loss = 8.5858e-01
*************** END FIRST PHASE ***************
* Training time: 4.322419166564941s
* Average epoch time: 0.04456102233572105s
************ MESH READING COMPLETE ************
* Dimension of the problem: 2D
* Elements type: t3: 3-node triangle
* Number of Dofs: 968
* Refinement level: 2
**************** START TRAINING ***************
epoch 2 loss = 8.4175e-01
epoch 3 loss = 8.4083e-01
epoch 4 loss = 8.4027e-01
epoch 5 loss = 8.3996e-01
epoch 6 loss = 8.3969e-01
epoch 7 loss = 8.395e-01
epoch 8 loss = 8.3930e-01
epoch 9 loss = 8.3913e-01
epoch 10 loss = 8.3893e-01
epoch 11 loss = 8.3875e-01
epoch 12 loss = 8.3866e-01
epoch 13 loss = 8.3856e-01
epoch 14 loss = 8.3848e-01
epoch 15 loss = 8.3839e-01
epoch 16 loss = 8.3835e-01
epoch 17 loss = 8.3827e-01
epoch 18 loss = 8.3824e-01
epoch 19 loss = 8.382e-01
epoch 20 loss = 8.3816e-01
epoch 21 loss = 8.3813e-01
epoch 22 loss = 8.3810e-01
epoch 23 loss = 8.3807e-01
epoch 24 loss = 8.3804e-01
epoch 25 loss = 8.3802e-01
epoch 26 loss = 8.38e-01
epoch 27 loss = 8.3797e-01
epoch 28 loss = 8.3795e-01
epoch 29 loss = 8.3793e-01
epoch 30 loss = 8.3792e-01
epoch 31 loss = 8.3790e-01
epoch 32 loss = 8.3789e-01
epoch 33 loss = 8.3788e-01
epoch 34 loss = 8.3785e-01
epoch 35 loss = 8.3784e-01
epoch 36 loss = 8.3782e-01
epoch 37 loss = 8.3781e-01
epoch 38 loss = 8.3779e-01
epoch 39 loss = 8.3778e-01
epoch 40 loss = 8.3776e-01
epoch 41 loss = 8.3775e-01
epoch 42 loss = 8.3774e-01
epoch 43 loss = 8.3773e-01
epoch 44 loss = 8.3772e-01
epoch 45 loss = 8.3771e-01
epoch 46 loss = 8.3770e-01
epoch 47 loss = 8.3769e-01
epoch 48 loss = 8.3769e-01
epoch 49 loss = 8.3767e-01
epoch 50 loss = 8.3767e-01
epoch 51 loss = 8.3766e-01
epoch 52 loss = 8.3765e-01
epoch 53 loss = 8.3765e-01
epoch 54 loss = 8.3764e-01
epoch 55 loss = 8.3763e-01
epoch 56 loss = 8.3763e-01
epoch 57 loss = 8.3762e-01
epoch 58 loss = 8.3762e-01
epoch 59 loss = 8.3761e-01
epoch 60 loss = 8.3760e-01
epoch 61 loss = 8.376e-01
epoch 62 loss = 8.3759e-01
epoch 63 loss = 8.3759e-01
epoch 64 loss = 8.3759e-01
epoch 65 loss = 8.3758e-01
epoch 66 loss = 8.3758e-01
epoch 67 loss = 8.3757e-01
epoch 68 loss = 8.3757e-01
epoch 69 loss = 8.3756e-01
epoch 70 loss = 8.3756e-01
epoch 71 loss = 8.3755e-01
epoch 72 loss = 8.3755e-01
epoch 73 loss = 8.3754e-01
epoch 74 loss = 8.3754e-01
epoch 75 loss = 8.3753e-01
epoch 76 loss = 8.3753e-01
epoch 77 loss = 8.3753e-01
epoch 78 loss = 8.3752e-01
epoch 79 loss = 8.3752e-01
epoch 80 loss = 8.3752e-01
epoch 81 loss = 8.3751e-01
epoch 82 loss = 8.3751e-01
epoch 83 loss = 8.3751e-01
epoch 84 loss = 8.3751e-01
epoch 85 loss = 8.3751e-01
*************** END FIRST PHASE ***************
* Training time: 4.471739292144775s
* Average epoch time: 0.05260869755464442s
************ MESH READING COMPLETE ************
* Dimension of the problem: 2D
* Elements type: t3: 3-node triangle
* Number of Dofs: 3608
* Refinement level: 3
**************** START TRAINING ***************
epoch 2 loss = 8.3261e-01
epoch 3 loss = 8.3246e-01
epoch 4 loss = 8.3234e-01
epoch 5 loss = 8.3227e-01
epoch 6 loss = 8.3220e-01
epoch 7 loss = 8.3213e-01
epoch 8 loss = 8.3208e-01
epoch 9 loss = 8.3204e-01
epoch 10 loss = 8.3201e-01
epoch 11 loss = 8.3196e-01
epoch 12 loss = 8.3194e-01
epoch 13 loss = 8.3191e-01
epoch 14 loss = 8.3189e-01
epoch 15 loss = 8.3187e-01
epoch 16 loss = 8.3185e-01
epoch 17 loss = 8.3183e-01
epoch 18 loss = 8.3182e-01
epoch 19 loss = 8.3180e-01
epoch 20 loss = 8.3179e-01
epoch 21 loss = 8.3178e-01
epoch 22 loss = 8.3176e-01
epoch 23 loss = 8.3176e-01
epoch 24 loss = 8.3174e-01
epoch 25 loss = 8.3174e-01
epoch 26 loss = 8.3172e-01
epoch 27 loss = 8.3171e-01
epoch 28 loss = 8.3170e-01
epoch 29 loss = 8.317e-01
epoch 30 loss = 8.3169e-01
epoch 31 loss = 8.3168e-01
epoch 32 loss = 8.3167e-01
epoch 33 loss = 8.3167e-01
epoch 34 loss = 8.3166e-01
epoch 35 loss = 8.3165e-01
epoch 36 loss = 8.3165e-01
epoch 37 loss = 8.3164e-01
epoch 38 loss = 8.3164e-01
epoch 39 loss = 8.3163e-01
epoch 40 loss = 8.3163e-01
epoch 41 loss = 8.3162e-01
epoch 42 loss = 8.3162e-01
epoch 43 loss = 8.3161e-01
epoch 44 loss = 8.3161e-01
epoch 45 loss = 8.3160e-01
epoch 46 loss = 8.316e-01
epoch 47 loss = 8.3159e-01
epoch 48 loss = 8.3159e-01
epoch 49 loss = 8.3158e-01
epoch 50 loss = 8.3158e-01
epoch 51 loss = 8.3157e-01
epoch 52 loss = 8.3157e-01
epoch 53 loss = 8.3157e-01
epoch 54 loss = 8.3156e-01
epoch 55 loss = 8.3156e-01
epoch 56 loss = 8.3156e-01
epoch 57 loss = 8.3156e-01
epoch 58 loss = 8.3155e-01
epoch 59 loss = 8.3155e-01
epoch 60 loss = 8.3155e-01
epoch 61 loss = 8.3154e-01
epoch 62 loss = 8.3154e-01
epoch 63 loss = 8.3154e-01
epoch 64 loss = 8.3153e-01
epoch 65 loss = 8.3153e-01
epoch 66 loss = 8.3153e-01
epoch 67 loss = 8.3153e-01
epoch 68 loss = 8.3152e-01
epoch 69 loss = 8.3152e-01
epoch 70 loss = 8.3152e-01
epoch 71 loss = 8.3152e-01
epoch 72 loss = 8.3151e-01
epoch 73 loss = 8.3151e-01
epoch 74 loss = 8.3151e-01
epoch 75 loss = 8.3151e-01
epoch 76 loss = 8.3150e-01
epoch 77 loss = 8.3150e-01
epoch 78 loss = 8.315e-01
epoch 79 loss = 8.315e-01
epoch 80 loss = 8.3149e-01
epoch 81 loss = 8.3149e-01
epoch 82 loss = 8.3149e-01
epoch 83 loss = 8.3149e-01
epoch 84 loss = 8.3149e-01
epoch 85 loss = 8.3148e-01
epoch 86 loss = 8.3148e-01
epoch 87 loss = 8.3148e-01
epoch 88 loss = 8.3148e-01
epoch 89 loss = 8.3147e-01
epoch 90 loss = 8.3147e-01
epoch 91 loss = 8.3147e-01
epoch 92 loss = 8.3147e-01
epoch 93 loss = 8.3147e-01
epoch 94 loss = 8.3146e-01
epoch 95 loss = 8.3146e-01
epoch 96 loss = 8.3146e-01
epoch 97 loss = 8.3146e-01
epoch 98 loss = 8.3146e-01
epoch 99 loss = 8.3146e-01
epoch 100 loss = 8.3145e-01
epoch 101 loss = 8.3145e-01
epoch 102 loss = 8.3145e-01
epoch 103 loss = 8.3145e-01
epoch 104 loss = 8.3145e-01
epoch 105 loss = 8.3145e-01
epoch 106 loss = 8.3145e-01
epoch 107 loss = 8.3144e-01
epoch 108 loss = 8.3144e-01
epoch 109 loss = 8.3144e-01
epoch 110 loss = 8.3144e-01
epoch 111 loss = 8.3144e-01
epoch 112 loss = 8.3144e-01
epoch 113 loss = 8.3143e-01
epoch 114 loss = 8.3143e-01
epoch 115 loss = 8.3143e-01
epoch 116 loss = 8.3143e-01
epoch 117 loss = 8.3143e-01
epoch 118 loss = 8.3143e-01
epoch 119 loss = 8.3143e-01
epoch 120 loss = 8.3143e-01
epoch 121 loss = 8.3142e-01
epoch 122 loss = 8.3142e-01
epoch 123 loss = 8.3142e-01
epoch 124 loss = 8.3142e-01
epoch 125 loss = 8.3142e-01
epoch 126 loss = 8.3142e-01
epoch 127 loss = 8.3142e-01
epoch 128 loss = 8.3142e-01
epoch 129 loss = 8.3141e-01
epoch 130 loss = 8.3141e-01
epoch 131 loss = 8.3141e-01
epoch 132 loss = 8.3141e-01
epoch 133 loss = 8.3141e-01
epoch 134 loss = 8.3141e-01
epoch 135 loss = 8.3141e-01
epoch 136 loss = 8.3141e-01
epoch 137 loss = 8.3141e-01
epoch 138 loss = 8.3141e-01
epoch 139 loss = 8.3140e-01
epoch 140 loss = 8.3140e-01
epoch 141 loss = 8.3140e-01
epoch 142 loss = 8.3140e-01
epoch 143 loss = 8.3140e-01
epoch 144 loss = 8.314e-01
epoch 145 loss = 8.314e-01
epoch 146 loss = 8.314e-01
epoch 147 loss = 8.314e-01
epoch 148 loss = 8.314e-01
epoch 149 loss = 8.314e-01
epoch 150 loss = 8.3139e-01
epoch 151 loss = 8.3139e-01
epoch 152 loss = 8.3139e-01
epoch 153 loss = 8.3139e-01
epoch 154 loss = 8.3139e-01
epoch 155 loss = 8.3139e-01
epoch 156 loss = 8.3139e-01
epoch 157 loss = 8.3139e-01
epoch 158 loss = 8.3139e-01
epoch 159 loss = 8.3139e-01
epoch 160 loss = 8.3139e-01
epoch 161 loss = 8.3139e-01
epoch 162 loss = 8.3139e-01
epoch 163 loss = 8.3138e-01
epoch 164 loss = 8.3138e-01
epoch 165 loss = 8.3138e-01
epoch 166 loss = 8.3138e-01
epoch 167 loss = 8.3138e-01
epoch 168 loss = 8.3138e-01
epoch 169 loss = 8.3138e-01
epoch 170 loss = 8.3138e-01
epoch 171 loss = 8.3138e-01
epoch 172 loss = 8.3138e-01
epoch 173 loss = 8.3138e-01
epoch 174 loss = 8.3138e-01
epoch 175 loss = 8.3138e-01
epoch 176 loss = 8.3138e-01
epoch 177 loss = 8.3138e-01
epoch 178 loss = 8.3137e-01
epoch 179 loss = 8.3137e-01
epoch 180 loss = 8.3137e-01
epoch 181 loss = 8.3137e-01
epoch 182 loss = 8.3137e-01
epoch 183 loss = 8.3137e-01
epoch 184 loss = 8.3137e-01
epoch 185 loss = 8.3137e-01
epoch 186 loss = 8.3137e-01
epoch 187 loss = 8.3137e-01
epoch 188 loss = 8.3137e-01
epoch 189 loss = 8.3137e-01
epoch 190 loss = 8.3137e-01
epoch 191 loss = 8.3137e-01
epoch 192 loss = 8.3137e-01
epoch 193 loss = 8.3137e-01
epoch 194 loss = 8.3137e-01
epoch 195 loss = 8.3136e-01
epoch 196 loss = 8.3136e-01
epoch 197 loss = 8.3136e-01
epoch 198 loss = 8.3136e-01
epoch 199 loss = 8.3136e-01
epoch 200 loss = 8.3136e-01
epoch 201 loss = 8.3136e-01
epoch 202 loss = 8.3136e-01
epoch 203 loss = 8.3136e-01
epoch 204 loss = 8.3136e-01
epoch 205 loss = 8.3136e-01
epoch 206 loss = 8.3136e-01
*************** END FIRST PHASE ***************
* Training time: 19.095026969909668s
* Average epoch time: 0.09269430567917314s
***************** END TRAINING ****************
* Training time: 28.10957956314087s
_ _ ____ ___ __ __
| \ | | ___ _ _| _ \ / _ \| \/ |
| \| |/ _ \ | | | |_) | | | | |\/| |
| |\ | __/ |_| | _ <| |_| | | | |
|_| \_|\___|\__,_|_| \_\ ___/|_| |_|
2024.09.18
************ MESH READING COMPLETE ************
* Dimension of the problem: 2D
* Elements type: t3: 3-node triangle
* Number of Dofs: 88
* Refinement level: 0
**************** START TRAINING ***************
epoch 2 loss = 6.4619e+00
epoch 50 loss = 5.1704e+00
epoch 100 loss = 4.1221e+00
epoch 150 loss = 3.3192e+00
epoch 200 loss = 2.7077e+00
epoch 250 loss = 2.2451e+00
epoch 300 loss = 1.8976e+00
epoch 350 loss = 1.6385e+00
epoch 400 loss = 1.4464e+00
epoch 450 loss = 1.3049e+00
epoch 500 loss = 1.2011e+00
epoch 550 loss = 1.1253e+00
epoch 600 loss = 1.0702e+00
epoch 650 loss = 1.0304e+00
epoch 700 loss = 1.0018e+00
epoch 750 loss = 9.814e-01
epoch 800 loss = 9.6694e-01
epoch 850 loss = 9.5677e-01
epoch 900 loss = 9.4965e-01
epoch 950 loss = 9.447e-01
epoch 1000 loss = 9.4126e-01
epoch 1050 loss = 9.3889e-01
epoch 1100 loss = 9.3724e-01
epoch 1150 loss = 9.361e-01
epoch 1200 loss = 9.353e-01
epoch 1250 loss = 9.3473e-01
epoch 1300 loss = 9.3431e-01
epoch 1350 loss = 9.3401e-01
epoch 1400 loss = 9.3378e-01
epoch 1450 loss = 9.336e-01
epoch 1500 loss = 9.3345e-01
epoch 1550 loss = 9.3333e-01
epoch 1600 loss = 9.3323e-01
epoch 1650 loss = 9.3315e-01
epoch 1700 loss = 9.3308e-01
epoch 1750 loss = 9.3301e-01
epoch 1800 loss = 9.3295e-01
epoch 1850 loss = 9.329e-01
epoch 1900 loss = 9.3285e-01
epoch 1950 loss = 9.3281e-01
epoch 2000 loss = 9.3276e-01
epoch 2050 loss = 9.3273e-01
epoch 2100 loss = 9.3269e-01
epoch 2150 loss = 9.3265e-01
epoch 2200 loss = 9.3261e-01
epoch 2250 loss = 9.3258e-01
epoch 2300 loss = 9.3254e-01
epoch 2350 loss = 9.3251e-01
epoch 2400 loss = 9.3247e-01
epoch 2450 loss = 9.3244e-01
epoch 2500 loss = 9.3240e-01
epoch 2550 loss = 9.3237e-01
epoch 2600 loss = 9.3233e-01
epoch 2650 loss = 9.3229e-01
epoch 2700 loss = 9.3226e-01
epoch 2750 loss = 9.3222e-01
epoch 2800 loss = 9.3218e-01
epoch 2850 loss = 9.3215e-01
epoch 2900 loss = 9.3211e-01
epoch 2950 loss = 9.3207e-01
epoch 3000 loss = 9.3204e-01
epoch 3050 loss = 9.32e-01
epoch 3100 loss = 9.3196e-01
epoch 3150 loss = 9.3192e-01
epoch 3200 loss = 9.3188e-01
epoch 3250 loss = 9.3185e-01
epoch 3300 loss = 9.3181e-01
epoch 3350 loss = 9.3177e-01
epoch 3400 loss = 9.3173e-01
epoch 3450 loss = 9.3169e-01
epoch 3500 loss = 9.3165e-01
epoch 3550 loss = 9.3161e-01
epoch 3600 loss = 9.3158e-01
epoch 3650 loss = 9.3154e-01
epoch 3700 loss = 9.315e-01
epoch 3750 loss = 9.3146e-01
epoch 3800 loss = 9.3142e-01
epoch 3850 loss = 9.3139e-01
epoch 3900 loss = 9.3135e-01
epoch 3950 loss = 9.3131e-01
epoch 4000 loss = 9.3127e-01
epoch 4050 loss = 9.3124e-01
epoch 4100 loss = 9.3120e-01
epoch 4150 loss = 9.3117e-01
epoch 4200 loss = 9.3113e-01
epoch 4250 loss = 9.311e-01
epoch 4300 loss = 9.3106e-01
epoch 4350 loss = 9.3103e-01
epoch 4400 loss = 9.3099e-01
epoch 4450 loss = 9.3096e-01
epoch 4500 loss = 9.3093e-01
epoch 4550 loss = 9.309e-01
epoch 4600 loss = 9.3087e-01
epoch 4650 loss = 9.3084e-01
epoch 4700 loss = 9.3081e-01
epoch 4750 loss = 9.3078e-01
epoch 4800 loss = 9.3075e-01
epoch 4850 loss = 9.3072e-01
epoch 4900 loss = 9.3069e-01
epoch 4950 loss = 9.3067e-01
epoch 5000 loss = 9.3064e-01
epoch 5050 loss = 9.3062e-01
epoch 5100 loss = 9.3059e-01
epoch 5150 loss = 9.3057e-01
epoch 5200 loss = 9.3055e-01
epoch 5250 loss = 9.3053e-01
epoch 5300 loss = 9.3051e-01
epoch 5350 loss = 9.3049e-01
epoch 5400 loss = 9.3047e-01
epoch 5450 loss = 9.3045e-01
epoch 5500 loss = 9.3043e-01
epoch 5550 loss = 9.3041e-01
epoch 5600 loss = 9.304e-01
epoch 5650 loss = 9.3038e-01
epoch 5700 loss = 9.3037e-01
epoch 5750 loss = 9.3035e-01
epoch 5800 loss = 9.3034e-01
epoch 5850 loss = 9.3032e-01
epoch 5900 loss = 9.3031e-01
epoch 5950 loss = 9.3030e-01
epoch 6000 loss = 9.3029e-01
epoch 6050 loss = 9.3028e-01
epoch 6100 loss = 9.3027e-01
epoch 6150 loss = 9.3026e-01
epoch 6200 loss = 9.3025e-01
epoch 6250 loss = 9.3024e-01
epoch 6300 loss = 9.3023e-01
epoch 6320 loss = 9.3023e-01
*************** END FIRST PHASE ***************
* Training time: 16.574242115020752s
* Average epoch time: 0.0026225066637691062s
************ MESH READING COMPLETE ************
* Dimension of the problem: 2D
* Elements type: t3: 3-node triangle
* Number of Dofs: 288
* Refinement level: 1
**************** START TRAINING ***************
epoch 2 loss = 8.9251e-01
epoch 50 loss = 8.7081e-01
epoch 100 loss = 8.6841e-01
epoch 150 loss = 8.6669e-01
epoch 200 loss = 8.6521e-01
epoch 250 loss = 8.6392e-01
epoch 300 loss = 8.6283e-01
epoch 350 loss = 8.6196e-01
epoch 400 loss = 8.6128e-01
epoch 450 loss = 8.6076e-01
epoch 500 loss = 8.6036e-01
epoch 550 loss = 8.6006e-01
epoch 600 loss = 8.5983e-01
epoch 650 loss = 8.5963e-01
epoch 700 loss = 8.5947e-01
epoch 750 loss = 8.5935e-01
epoch 800 loss = 8.5924e-01
epoch 850 loss = 8.5916e-01
epoch 900 loss = 8.5908e-01
epoch 950 loss = 8.5902e-01
epoch 1000 loss = 8.5897e-01
epoch 1050 loss = 8.5892e-01
epoch 1100 loss = 8.5888e-01
epoch 1150 loss = 8.5884e-01
epoch 1200 loss = 8.588e-01
epoch 1250 loss = 8.5876e-01
epoch 1300 loss = 8.5873e-01
epoch 1350 loss = 8.5870e-01
epoch 1400 loss = 8.5867e-01
epoch 1450 loss = 8.5865e-01
epoch 1458 loss = 8.5865e-01
*************** END FIRST PHASE ***************
* Training time: 3.947120189666748s
* Average epoch time: 0.002707215493598593s
************ MESH READING COMPLETE ************
* Dimension of the problem: 2D
* Elements type: t3: 3-node triangle
* Number of Dofs: 968
* Refinement level: 2
**************** START TRAINING ***************
epoch 2 loss = 8.4782e-01
epoch 50 loss = 8.4136e-01
epoch 100 loss = 8.4051e-01
epoch 150 loss = 8.3992e-01
epoch 200 loss = 8.3949e-01
epoch 250 loss = 8.3919e-01
epoch 300 loss = 8.3897e-01
epoch 350 loss = 8.3879e-01
epoch 400 loss = 8.3865e-01
epoch 450 loss = 8.3851e-01
epoch 500 loss = 8.3839e-01
epoch 550 loss = 8.3827e-01
epoch 600 loss = 8.3817e-01
epoch 650 loss = 8.3809e-01
epoch 700 loss = 8.3802e-01
epoch 750 loss = 8.3796e-01
epoch 800 loss = 8.3791e-01
epoch 833 loss = 8.3789e-01
*************** END FIRST PHASE ***************
* Training time: 2.75766658782959s
* Average epoch time: 0.0033105241150415245s
************ MESH READING COMPLETE ************
* Dimension of the problem: 2D
* Elements type: t3: 3-node triangle
* Number of Dofs: 3608
* Refinement level: 3
**************** START TRAINING ***************
epoch 2 loss = 8.435e-01
epoch 50 loss = 8.3246e-01
epoch 100 loss = 8.3225e-01
epoch 150 loss = 8.3212e-01
epoch 200 loss = 8.3204e-01
epoch 250 loss = 8.3197e-01
epoch 300 loss = 8.3192e-01
epoch 330 loss = 8.319e-01
*************** END FIRST PHASE ***************
* Training time: 1.9150269031524658s
* Average epoch time: 0.005803111827734745s
************ MESH READING COMPLETE ************
* Dimension of the problem: 2D
* Elements type: t3: 3-node triangle
* Number of Dofs: 14176
* Refinement level: 4
**************** START TRAINING ***************
epoch 2 loss = 8.9117e-01
epoch 50 loss = 8.3014e-01
epoch 100 loss = 8.2995e-01
epoch 150 loss = 8.2992e-01
epoch 170 loss = 8.2992e-01
*************** END FIRST PHASE ***************
* Training time: 2.108024835586548s
* Average epoch time: 0.012400146091685575s
***************** END TRAINING ****************
* Training time: 27.302080631256104s
_ _ ____ ___ __ __
| \ | | ___ _ _| _ \ / _ \| \/ |
| \| |/ _ \ | | | |_) | | | | |\/| |
| |\ | __/ |_| | _ <| |_| | | | |
|_| \_|\___|\__,_|_| \_\ ___/|_| |_|
2024.09.18
************ MESH READING COMPLETE ************
* Dimension of the problem: 2D
* Elements type: t3: 3-node triangle
* Number of Dofs: 88
* Refinement level: 0
**************** START TRAINING ***************
epoch 2 loss = 9.4093e-01
epoch 3 loss = 9.3267e-01
epoch 4 loss = 9.3022e-01
epoch 5 loss = 9.3013e-01
epoch 6 loss = 9.3012e-01
epoch 7 loss = 9.3012e-01
epoch 8 loss = 9.3012e-01
*************** END FIRST PHASE ***************
* Training time: 0.22570419311523438s
* Average epoch time: 0.028213024139404297s
************ MESH READING COMPLETE ************
* Dimension of the problem: 2D
* Elements type: t3: 3-node triangle
* Number of Dofs: 288
* Refinement level: 1
**************** START TRAINING ***************
epoch 2 loss = 8.6992e-01
epoch 3 loss = 8.6361e-01
epoch 4 loss = 8.6262e-01
epoch 5 loss = 8.6182e-01
epoch 6 loss = 8.6080e-01
epoch 7 loss = 8.6019e-01
epoch 8 loss = 8.5992e-01
epoch 9 loss = 8.5959e-01
epoch 10 loss = 8.5934e-01
epoch 11 loss = 8.5925e-01
epoch 12 loss = 8.5913e-01
epoch 13 loss = 8.5906e-01
epoch 14 loss = 8.5902e-01
epoch 15 loss = 8.5899e-01
epoch 16 loss = 8.5895e-01
epoch 17 loss = 8.5894e-01
epoch 18 loss = 8.589e-01
epoch 19 loss = 8.5889e-01
epoch 20 loss = 8.5887e-01
epoch 21 loss = 8.5885e-01
epoch 22 loss = 8.5883e-01
epoch 23 loss = 8.5881e-01
epoch 24 loss = 8.5879e-01
epoch 25 loss = 8.5878e-01
epoch 26 loss = 8.5877e-01
epoch 27 loss = 8.5874e-01
epoch 28 loss = 8.5873e-01
epoch 29 loss = 8.5871e-01
epoch 30 loss = 8.5869e-01
epoch 31 loss = 8.5867e-01
epoch 32 loss = 8.5865e-01
epoch 33 loss = 8.5864e-01
epoch 34 loss = 8.5863e-01
epoch 35 loss = 8.5862e-01
epoch 36 loss = 8.5862e-01
epoch 37 loss = 8.5861e-01
epoch 38 loss = 8.5861e-01
epoch 39 loss = 8.5861e-01
epoch 40 loss = 8.5861e-01
epoch 41 loss = 8.5861e-01
epoch 42 loss = 8.5861e-01
epoch 43 loss = 8.5860e-01
epoch 44 loss = 8.5860e-01
epoch 45 loss = 8.5860e-01
epoch 46 loss = 8.5860e-01
epoch 47 loss = 8.5860e-01
epoch 48 loss = 8.5860e-01
epoch 49 loss = 8.5860e-01
epoch 50 loss = 8.5860e-01
epoch 51 loss = 8.5860e-01
epoch 52 loss = 8.5860e-01
epoch 53 loss = 8.5860e-01
epoch 54 loss = 8.586e-01
epoch 55 loss = 8.586e-01
epoch 56 loss = 8.586e-01
epoch 57 loss = 8.586e-01
epoch 58 loss = 8.586e-01
epoch 59 loss = 8.586e-01
epoch 60 loss = 8.586e-01
epoch 61 loss = 8.586e-01
epoch 62 loss = 8.586e-01
epoch 63 loss = 8.586e-01
epoch 64 loss = 8.586e-01
epoch 65 loss = 8.586e-01
epoch 66 loss = 8.586e-01
epoch 67 loss = 8.586e-01
epoch 68 loss = 8.586e-01
epoch 69 loss = 8.586e-01
epoch 70 loss = 8.586e-01
epoch 71 loss = 8.5859e-01
epoch 72 loss = 8.5859e-01
epoch 73 loss = 8.5859e-01
epoch 74 loss = 8.5859e-01
epoch 75 loss = 8.5859e-01
epoch 76 loss = 8.5859e-01
epoch 77 loss = 8.5859e-01
epoch 78 loss = 8.5859e-01
epoch 79 loss = 8.5859e-01
epoch 80 loss = 8.5858e-01
epoch 81 loss = 8.5858e-01
epoch 82 loss = 8.5858e-01
epoch 83 loss = 8.5858e-01
epoch 84 loss = 8.5858e-01
epoch 85 loss = 8.5858e-01
epoch 86 loss = 8.5858e-01
epoch 87 loss = 8.5858e-01
epoch 88 loss = 8.5858e-01
epoch 89 loss = 8.5858e-01
epoch 90 loss = 8.5858e-01
epoch 91 loss = 8.5858e-01
epoch 92 loss = 8.5858e-01
epoch 93 loss = 8.5858e-01
epoch 94 loss = 8.5858e-01
epoch 95 loss = 8.5858e-01
epoch 96 loss = 8.5858e-01
epoch 97 loss = 8.5858e-01
*************** END FIRST PHASE ***************
* Training time: 4.291126012802124s
* Average epoch time: 0.04423841250311468s
************ MESH READING COMPLETE ************
* Dimension of the problem: 2D
* Elements type: t3: 3-node triangle
* Number of Dofs: 968
* Refinement level: 2
**************** START TRAINING ***************
epoch 2 loss = 8.4175e-01
epoch 3 loss = 8.4083e-01
epoch 4 loss = 8.4027e-01
epoch 5 loss = 8.3996e-01
epoch 6 loss = 8.3969e-01
epoch 7 loss = 8.395e-01
epoch 8 loss = 8.3930e-01
epoch 9 loss = 8.3913e-01
epoch 10 loss = 8.3893e-01
epoch 11 loss = 8.3875e-01
epoch 12 loss = 8.3866e-01
epoch 13 loss = 8.3856e-01
epoch 14 loss = 8.3848e-01
epoch 15 loss = 8.3839e-01
epoch 16 loss = 8.3835e-01
epoch 17 loss = 8.3827e-01
epoch 18 loss = 8.3824e-01
epoch 19 loss = 8.382e-01
epoch 20 loss = 8.3816e-01
epoch 21 loss = 8.3813e-01
epoch 22 loss = 8.3810e-01
epoch 23 loss = 8.3807e-01
epoch 24 loss = 8.3804e-01
epoch 25 loss = 8.3802e-01
epoch 26 loss = 8.38e-01
epoch 27 loss = 8.3797e-01
epoch 28 loss = 8.3795e-01
epoch 29 loss = 8.3793e-01
epoch 30 loss = 8.3792e-01
epoch 31 loss = 8.3790e-01
epoch 32 loss = 8.3789e-01
epoch 33 loss = 8.3788e-01
epoch 34 loss = 8.3785e-01
epoch 35 loss = 8.3784e-01
epoch 36 loss = 8.3782e-01
epoch 37 loss = 8.3781e-01
epoch 38 loss = 8.3779e-01
epoch 39 loss = 8.3778e-01
epoch 40 loss = 8.3776e-01
epoch 41 loss = 8.3775e-01
epoch 42 loss = 8.3774e-01
epoch 43 loss = 8.3773e-01
epoch 44 loss = 8.3772e-01
epoch 45 loss = 8.3771e-01
epoch 46 loss = 8.3770e-01
epoch 47 loss = 8.3769e-01
epoch 48 loss = 8.3769e-01
epoch 49 loss = 8.3767e-01
epoch 50 loss = 8.3767e-01
epoch 51 loss = 8.3766e-01
epoch 52 loss = 8.3765e-01
epoch 53 loss = 8.3765e-01
epoch 54 loss = 8.3764e-01
epoch 55 loss = 8.3763e-01
epoch 56 loss = 8.3763e-01
epoch 57 loss = 8.3762e-01
epoch 58 loss = 8.3762e-01
epoch 59 loss = 8.3761e-01
epoch 60 loss = 8.3760e-01
epoch 61 loss = 8.376e-01
epoch 62 loss = 8.3759e-01
epoch 63 loss = 8.3759e-01
epoch 64 loss = 8.3759e-01
epoch 65 loss = 8.3758e-01
epoch 66 loss = 8.3758e-01
epoch 67 loss = 8.3757e-01
epoch 68 loss = 8.3757e-01
epoch 69 loss = 8.3756e-01
epoch 70 loss = 8.3756e-01
epoch 71 loss = 8.3755e-01
epoch 72 loss = 8.3755e-01
epoch 73 loss = 8.3754e-01
epoch 74 loss = 8.3754e-01
epoch 75 loss = 8.3753e-01
epoch 76 loss = 8.3753e-01
epoch 77 loss = 8.3753e-01
epoch 78 loss = 8.3752e-01
epoch 79 loss = 8.3752e-01
epoch 80 loss = 8.3752e-01
epoch 81 loss = 8.3751e-01
epoch 82 loss = 8.3751e-01
epoch 83 loss = 8.3751e-01
epoch 84 loss = 8.3751e-01
epoch 85 loss = 8.3751e-01
*************** END FIRST PHASE ***************
* Training time: 4.4527130126953125s
* Average epoch time: 0.05238485897288603s
************ MESH READING COMPLETE ************
* Dimension of the problem: 2D
* Elements type: t3: 3-node triangle
* Number of Dofs: 3608
* Refinement level: 3
**************** START TRAINING ***************
epoch 2 loss = 8.3261e-01
epoch 3 loss = 8.3246e-01
epoch 4 loss = 8.3234e-01
epoch 5 loss = 8.3227e-01
epoch 6 loss = 8.3220e-01
epoch 7 loss = 8.3213e-01
epoch 8 loss = 8.3208e-01
epoch 9 loss = 8.3204e-01
epoch 10 loss = 8.3201e-01
epoch 11 loss = 8.3196e-01
epoch 12 loss = 8.3194e-01
epoch 13 loss = 8.3191e-01
epoch 14 loss = 8.3189e-01
epoch 15 loss = 8.3187e-01
epoch 16 loss = 8.3185e-01
epoch 17 loss = 8.3183e-01
epoch 18 loss = 8.3182e-01
epoch 19 loss = 8.3180e-01
epoch 20 loss = 8.3179e-01
epoch 21 loss = 8.3178e-01
epoch 22 loss = 8.3176e-01
epoch 23 loss = 8.3176e-01
epoch 24 loss = 8.3174e-01
epoch 25 loss = 8.3174e-01
epoch 26 loss = 8.3172e-01
epoch 27 loss = 8.3171e-01
epoch 28 loss = 8.3170e-01
epoch 29 loss = 8.317e-01
epoch 30 loss = 8.3169e-01
epoch 31 loss = 8.3168e-01
epoch 32 loss = 8.3167e-01
epoch 33 loss = 8.3167e-01
epoch 34 loss = 8.3166e-01
epoch 35 loss = 8.3165e-01
epoch 36 loss = 8.3165e-01
epoch 37 loss = 8.3164e-01
epoch 38 loss = 8.3164e-01
epoch 39 loss = 8.3163e-01
epoch 40 loss = 8.3163e-01
epoch 41 loss = 8.3162e-01
epoch 42 loss = 8.3162e-01
epoch 43 loss = 8.3161e-01
epoch 44 loss = 8.3161e-01
epoch 45 loss = 8.3160e-01
epoch 46 loss = 8.316e-01
epoch 47 loss = 8.3159e-01
epoch 48 loss = 8.3159e-01
epoch 49 loss = 8.3158e-01
epoch 50 loss = 8.3158e-01
epoch 51 loss = 8.3157e-01
epoch 52 loss = 8.3157e-01
epoch 53 loss = 8.3157e-01
epoch 54 loss = 8.3156e-01
epoch 55 loss = 8.3156e-01
epoch 56 loss = 8.3156e-01
epoch 57 loss = 8.3156e-01
epoch 58 loss = 8.3155e-01
epoch 59 loss = 8.3155e-01
epoch 60 loss = 8.3155e-01
epoch 61 loss = 8.3154e-01
epoch 62 loss = 8.3154e-01
epoch 63 loss = 8.3154e-01
epoch 64 loss = 8.3153e-01
epoch 65 loss = 8.3153e-01
epoch 66 loss = 8.3153e-01
epoch 67 loss = 8.3153e-01
epoch 68 loss = 8.3152e-01
epoch 69 loss = 8.3152e-01
epoch 70 loss = 8.3152e-01
epoch 71 loss = 8.3152e-01
epoch 72 loss = 8.3151e-01
epoch 73 loss = 8.3151e-01
epoch 74 loss = 8.3151e-01
epoch 75 loss = 8.3151e-01
epoch 76 loss = 8.3150e-01
epoch 77 loss = 8.3150e-01
epoch 78 loss = 8.315e-01
epoch 79 loss = 8.315e-01
epoch 80 loss = 8.3149e-01
epoch 81 loss = 8.3149e-01
epoch 82 loss = 8.3149e-01
epoch 83 loss = 8.3149e-01
epoch 84 loss = 8.3149e-01
epoch 85 loss = 8.3148e-01
epoch 86 loss = 8.3148e-01
epoch 87 loss = 8.3148e-01
epoch 88 loss = 8.3148e-01
epoch 89 loss = 8.3147e-01
epoch 90 loss = 8.3147e-01
epoch 91 loss = 8.3147e-01
epoch 92 loss = 8.3147e-01
epoch 93 loss = 8.3147e-01
epoch 94 loss = 8.3146e-01
epoch 95 loss = 8.3146e-01
epoch 96 loss = 8.3146e-01
epoch 97 loss = 8.3146e-01
epoch 98 loss = 8.3146e-01
epoch 99 loss = 8.3146e-01
epoch 100 loss = 8.3145e-01
epoch 101 loss = 8.3145e-01
epoch 102 loss = 8.3145e-01
epoch 103 loss = 8.3145e-01
epoch 104 loss = 8.3145e-01
epoch 105 loss = 8.3145e-01
epoch 106 loss = 8.3145e-01
epoch 107 loss = 8.3144e-01
epoch 108 loss = 8.3144e-01
epoch 109 loss = 8.3144e-01
epoch 110 loss = 8.3144e-01
epoch 111 loss = 8.3144e-01
epoch 112 loss = 8.3144e-01
epoch 113 loss = 8.3143e-01
epoch 114 loss = 8.3143e-01
epoch 115 loss = 8.3143e-01
epoch 116 loss = 8.3143e-01
epoch 117 loss = 8.3143e-01
epoch 118 loss = 8.3143e-01
epoch 119 loss = 8.3143e-01
epoch 120 loss = 8.3143e-01
epoch 121 loss = 8.3142e-01
epoch 122 loss = 8.3142e-01
epoch 123 loss = 8.3142e-01
epoch 124 loss = 8.3142e-01
epoch 125 loss = 8.3142e-01
epoch 126 loss = 8.3142e-01
epoch 127 loss = 8.3142e-01
epoch 128 loss = 8.3142e-01
epoch 129 loss = 8.3141e-01
epoch 130 loss = 8.3141e-01
epoch 131 loss = 8.3141e-01
epoch 132 loss = 8.3141e-01
epoch 133 loss = 8.3141e-01
epoch 134 loss = 8.3141e-01
epoch 135 loss = 8.3141e-01
epoch 136 loss = 8.3141e-01
epoch 137 loss = 8.3141e-01
epoch 138 loss = 8.3141e-01
epoch 139 loss = 8.3140e-01
epoch 140 loss = 8.3140e-01
epoch 141 loss = 8.3140e-01
epoch 142 loss = 8.3140e-01
epoch 143 loss = 8.3140e-01
epoch 144 loss = 8.314e-01
epoch 145 loss = 8.314e-01
epoch 146 loss = 8.314e-01
epoch 147 loss = 8.314e-01
epoch 148 loss = 8.314e-01
epoch 149 loss = 8.314e-01
epoch 150 loss = 8.3139e-01
epoch 151 loss = 8.3139e-01
epoch 152 loss = 8.3139e-01
epoch 153 loss = 8.3139e-01
epoch 154 loss = 8.3139e-01
epoch 155 loss = 8.3139e-01
epoch 156 loss = 8.3139e-01
epoch 157 loss = 8.3139e-01
epoch 158 loss = 8.3139e-01
epoch 159 loss = 8.3139e-01
epoch 160 loss = 8.3139e-01
epoch 161 loss = 8.3139e-01
epoch 162 loss = 8.3139e-01
epoch 163 loss = 8.3138e-01
epoch 164 loss = 8.3138e-01
epoch 165 loss = 8.3138e-01
epoch 166 loss = 8.3138e-01
epoch 167 loss = 8.3138e-01
epoch 168 loss = 8.3138e-01
epoch 169 loss = 8.3138e-01
epoch 170 loss = 8.3138e-01
epoch 171 loss = 8.3138e-01
epoch 172 loss = 8.3138e-01
epoch 173 loss = 8.3138e-01
epoch 174 loss = 8.3138e-01
epoch 175 loss = 8.3138e-01
epoch 176 loss = 8.3138e-01
epoch 177 loss = 8.3138e-01
epoch 178 loss = 8.3137e-01
epoch 179 loss = 8.3137e-01
epoch 180 loss = 8.3137e-01
epoch 181 loss = 8.3137e-01
epoch 182 loss = 8.3137e-01
epoch 183 loss = 8.3137e-01
epoch 184 loss = 8.3137e-01
epoch 185 loss = 8.3137e-01
epoch 186 loss = 8.3137e-01
epoch 187 loss = 8.3137e-01
epoch 188 loss = 8.3137e-01
epoch 189 loss = 8.3137e-01
epoch 190 loss = 8.3137e-01
epoch 191 loss = 8.3137e-01
epoch 192 loss = 8.3137e-01
epoch 193 loss = 8.3137e-01
epoch 194 loss = 8.3137e-01
epoch 195 loss = 8.3136e-01
epoch 196 loss = 8.3136e-01
epoch 197 loss = 8.3136e-01
epoch 198 loss = 8.3136e-01
epoch 199 loss = 8.3136e-01
epoch 200 loss = 8.3136e-01
epoch 201 loss = 8.3136e-01
epoch 202 loss = 8.3136e-01
epoch 203 loss = 8.3136e-01
epoch 204 loss = 8.3136e-01
epoch 205 loss = 8.3136e-01
epoch 206 loss = 8.3136e-01
*************** END FIRST PHASE ***************
* Training time: 19.210541009902954s
* Average epoch time: 0.09325505344613085s
************ MESH READING COMPLETE ************
* Dimension of the problem: 2D
* Elements type: t3: 3-node triangle
* Number of Dofs: 14176
* Refinement level: 4
**************** START TRAINING ***************
epoch 2 loss = 8.3e-01
epoch 3 loss = 8.2998e-01
epoch 4 loss = 8.2995e-01
epoch 5 loss = 8.2994e-01
epoch 6 loss = 8.2993e-01
epoch 7 loss = 8.2991e-01
epoch 8 loss = 8.2990e-01
epoch 9 loss = 8.299e-01
epoch 10 loss = 8.2989e-01
epoch 11 loss = 8.2988e-01
epoch 12 loss = 8.2988e-01
epoch 13 loss = 8.2987e-01
epoch 14 loss = 8.2987e-01
epoch 15 loss = 8.2986e-01
epoch 16 loss = 8.2986e-01
epoch 17 loss = 8.2985e-01
epoch 18 loss = 8.2985e-01
epoch 19 loss = 8.2984e-01
epoch 20 loss = 8.2984e-01
epoch 21 loss = 8.2984e-01
epoch 22 loss = 8.2983e-01
epoch 23 loss = 8.2983e-01
epoch 24 loss = 8.2983e-01
epoch 25 loss = 8.2982e-01
epoch 26 loss = 8.2982e-01
epoch 27 loss = 8.2982e-01
epoch 28 loss = 8.2982e-01
epoch 29 loss = 8.2982e-01
epoch 30 loss = 8.2981e-01
epoch 31 loss = 8.2981e-01
epoch 32 loss = 8.2981e-01
epoch 33 loss = 8.2981e-01
epoch 34 loss = 8.2981e-01
epoch 35 loss = 8.2980e-01
epoch 36 loss = 8.2980e-01
epoch 37 loss = 8.2980e-01
epoch 38 loss = 8.298e-01
epoch 39 loss = 8.298e-01
epoch 40 loss = 8.298e-01
epoch 41 loss = 8.298e-01
epoch 42 loss = 8.2979e-01
epoch 43 loss = 8.2979e-01
epoch 44 loss = 8.2979e-01
epoch 45 loss = 8.2979e-01
epoch 46 loss = 8.2979e-01
epoch 47 loss = 8.2979e-01
epoch 48 loss = 8.2979e-01
epoch 49 loss = 8.2979e-01
epoch 50 loss = 8.2978e-01
epoch 51 loss = 8.2978e-01
epoch 52 loss = 8.2978e-01
epoch 53 loss = 8.2978e-01
epoch 54 loss = 8.2978e-01
epoch 55 loss = 8.2978e-01
epoch 56 loss = 8.2978e-01
epoch 57 loss = 8.2978e-01
epoch 58 loss = 8.2978e-01
epoch 59 loss = 8.2978e-01
epoch 60 loss = 8.2977e-01
epoch 61 loss = 8.2977e-01
epoch 62 loss = 8.2977e-01
epoch 63 loss = 8.2977e-01
epoch 64 loss = 8.2977e-01
epoch 65 loss = 8.2977e-01
epoch 66 loss = 8.2977e-01
epoch 67 loss = 8.2977e-01
epoch 68 loss = 8.2977e-01
epoch 69 loss = 8.2977e-01
epoch 70 loss = 8.2977e-01
epoch 71 loss = 8.2977e-01
epoch 72 loss = 8.2976e-01
epoch 73 loss = 8.2976e-01
epoch 74 loss = 8.2976e-01
epoch 75 loss = 8.2976e-01
epoch 76 loss = 8.2976e-01
epoch 77 loss = 8.2976e-01
epoch 78 loss = 8.2976e-01
epoch 79 loss = 8.2976e-01
epoch 80 loss = 8.2976e-01
epoch 81 loss = 8.2976e-01
epoch 82 loss = 8.2976e-01
epoch 83 loss = 8.2976e-01
epoch 84 loss = 8.2976e-01
epoch 85 loss = 8.2976e-01
epoch 86 loss = 8.2976e-01
epoch 87 loss = 8.2976e-01
epoch 88 loss = 8.2976e-01
epoch 89 loss = 8.2975e-01
epoch 90 loss = 8.2975e-01
epoch 91 loss = 8.2975e-01
epoch 92 loss = 8.2975e-01
epoch 93 loss = 8.2975e-01
epoch 94 loss = 8.2975e-01
epoch 95 loss = 8.2975e-01
epoch 96 loss = 8.2975e-01
epoch 97 loss = 8.2975e-01
epoch 98 loss = 8.2975e-01
epoch 99 loss = 8.2975e-01
epoch 100 loss = 8.2975e-01
epoch 101 loss = 8.2975e-01
epoch 102 loss = 8.2975e-01
epoch 103 loss = 8.2975e-01
epoch 104 loss = 8.2975e-01
epoch 105 loss = 8.2975e-01
epoch 106 loss = 8.2975e-01
epoch 107 loss = 8.2975e-01
epoch 108 loss = 8.2975e-01
epoch 109 loss = 8.2974e-01
epoch 110 loss = 8.2974e-01
epoch 111 loss = 8.2974e-01
epoch 112 loss = 8.2974e-01
epoch 113 loss = 8.2974e-01
epoch 114 loss = 8.2974e-01
epoch 115 loss = 8.2974e-01
epoch 116 loss = 8.2974e-01
epoch 117 loss = 8.2974e-01
epoch 118 loss = 8.2974e-01
epoch 119 loss = 8.2974e-01
epoch 120 loss = 8.2974e-01
epoch 121 loss = 8.2974e-01
epoch 122 loss = 8.2974e-01
epoch 123 loss = 8.2974e-01
epoch 124 loss = 8.2974e-01
epoch 125 loss = 8.2974e-01
epoch 126 loss = 8.2974e-01
epoch 127 loss = 8.2974e-01
epoch 128 loss = 8.2974e-01
epoch 129 loss = 8.2974e-01
epoch 130 loss = 8.2974e-01
epoch 131 loss = 8.2974e-01
epoch 132 loss = 8.2974e-01
epoch 133 loss = 8.2974e-01
epoch 134 loss = 8.2974e-01
epoch 135 loss = 8.2974e-01
epoch 136 loss = 8.2974e-01
epoch 137 loss = 8.2973e-01
epoch 138 loss = 8.2973e-01
epoch 139 loss = 8.2973e-01
epoch 140 loss = 8.2973e-01
epoch 141 loss = 8.2973e-01
epoch 142 loss = 8.2973e-01
epoch 143 loss = 8.2973e-01
epoch 144 loss = 8.2973e-01
epoch 145 loss = 8.2973e-01
epoch 146 loss = 8.2973e-01
epoch 147 loss = 8.2973e-01
epoch 148 loss = 8.2973e-01
epoch 149 loss = 8.2973e-01
epoch 150 loss = 8.2973e-01
epoch 151 loss = 8.2973e-01
epoch 152 loss = 8.2973e-01
epoch 153 loss = 8.2973e-01
epoch 154 loss = 8.2973e-01
epoch 155 loss = 8.2973e-01
epoch 156 loss = 8.2973e-01
epoch 157 loss = 8.2973e-01
epoch 158 loss = 8.2973e-01
epoch 159 loss = 8.2973e-01
epoch 160 loss = 8.2973e-01
epoch 161 loss = 8.2973e-01
epoch 162 loss = 8.2973e-01
epoch 163 loss = 8.2973e-01
epoch 164 loss = 8.2973e-01
epoch 165 loss = 8.2973e-01
epoch 166 loss = 8.2973e-01
epoch 167 loss = 8.2973e-01
epoch 168 loss = 8.2973e-01
epoch 169 loss = 8.2973e-01
epoch 170 loss = 8.2973e-01
epoch 171 loss = 8.2973e-01
epoch 172 loss = 8.2973e-01
epoch 173 loss = 8.2973e-01
epoch 174 loss = 8.2972e-01
epoch 175 loss = 8.2972e-01
epoch 176 loss = 8.2972e-01
epoch 177 loss = 8.2972e-01
epoch 178 loss = 8.2972e-01
epoch 179 loss = 8.2972e-01
epoch 180 loss = 8.2972e-01
epoch 181 loss = 8.2972e-01
epoch 182 loss = 8.2972e-01
epoch 183 loss = 8.2972e-01
epoch 184 loss = 8.2972e-01
epoch 185 loss = 8.2972e-01
epoch 186 loss = 8.2972e-01
epoch 187 loss = 8.2972e-01
epoch 188 loss = 8.2972e-01
epoch 189 loss = 8.2972e-01
epoch 190 loss = 8.2972e-01
epoch 191 loss = 8.2972e-01
epoch 192 loss = 8.2972e-01
epoch 193 loss = 8.2972e-01
epoch 194 loss = 8.2972e-01
epoch 195 loss = 8.2972e-01
epoch 196 loss = 8.2972e-01
epoch 197 loss = 8.2972e-01
epoch 198 loss = 8.2972e-01
epoch 199 loss = 8.2972e-01
epoch 200 loss = 8.2972e-01
epoch 201 loss = 8.2972e-01
epoch 202 loss = 8.2972e-01
epoch 203 loss = 8.2972e-01
epoch 204 loss = 8.2972e-01
epoch 205 loss = 8.2972e-01
epoch 206 loss = 8.2972e-01
epoch 207 loss = 8.2972e-01
epoch 208 loss = 8.2972e-01
epoch 209 loss = 8.2972e-01
epoch 210 loss = 8.2972e-01
epoch 211 loss = 8.2972e-01
epoch 212 loss = 8.2972e-01
epoch 213 loss = 8.2972e-01
epoch 214 loss = 8.2972e-01
epoch 215 loss = 8.2972e-01
epoch 216 loss = 8.2972e-01
epoch 217 loss = 8.2972e-01
epoch 218 loss = 8.2972e-01
epoch 219 loss = 8.2972e-01
epoch 220 loss = 8.2972e-01
epoch 221 loss = 8.2971e-01
epoch 222 loss = 8.2971e-01
epoch 223 loss = 8.2971e-01
epoch 224 loss = 8.2971e-01
epoch 225 loss = 8.2971e-01
epoch 226 loss = 8.2971e-01
epoch 227 loss = 8.2971e-01
epoch 228 loss = 8.2971e-01
epoch 229 loss = 8.2971e-01
epoch 230 loss = 8.2971e-01
epoch 231 loss = 8.2971e-01
epoch 232 loss = 8.2971e-01
epoch 233 loss = 8.2971e-01
epoch 234 loss = 8.2971e-01
epoch 235 loss = 8.2971e-01
*************** END FIRST PHASE ***************
* Training time: 47.95187306404114s
* Average epoch time: 0.2040505236767708s
***************** END TRAINING ****************
* Training time: 76.13195729255676s
print("Adam")
print("u = ", r_adapt_error_u[:,0])
print("v = ", r_adapt_error_v[:,0])
print("s = ", r_adapt_error_stress[:,0])
print("s max = ", r_adapt_error_stress_max[:,0])
print()
print("LBFGS")
print("u = ", r_adapt_error_u[:,1])
print("v = ", r_adapt_error_v[:,1])
print("s = ", r_adapt_error_stress[:,1])
print("s max = ", r_adapt_error_stress_max[:,1])
Adam
u = [0.19452372 0.07560274 0.02455851 0.00979111 0.00865349]
v = [0.02543295 0.00813394 0.00240461 0.00074863 0.0004539 ]
s = [0.21910054 0.11351486 0.06748505 0.03945204 0.02177264]
s max = [0.68692235 0.87717278 1.01535961 0.99544879 1.01598016]
LBFGS
u = [0.1964758 0.07483696 0.02378997 0.00619278 0.00083231]
v = [0.02515145 0.00827428 0.00241591 0.00058403 0.00012013]
s = [0.2195726 0.11374515 0.06448546 0.03514965 0.01992995]
s max = [0.68675181 0.87801194 0.95702206 0.99308209 0.9965984 ]
# Plot normalized displacement error
fig = matplotlib.pyplot.gcf()
ax = plt.gca()
plt.plot(mesh_resolution, error_u[:,1],'-', color = "darkblue", label = r'$\| e_{u_x}\|_2$'+ ", L-BFGS")
plt.plot(mesh_resolution, error_v[:,1],'-', color = "purple", label = r'$\| e_{u_y}\|_2$'+ ", L-BFGS")
plt.plot(mesh_resolution, error_stress[:,1],'-', color = "green", label = r'$\| e_{\sigma_{VM}}\|_2$'+ ", L-BFGS")
plt.plot(mesh_resolution, r_adapt_error_u[:,1],'--', color = "darkblue", label = r'$\| e_{u_x}\|_2$'+ ", L-BFGS, r-adapt.")
plt.plot(mesh_resolution, r_adapt_error_v[:,1],'--', color = "purple", label = r'$\| e_{u_y}\|_2$'+ ", L-BFGS, r-adapt.")
plt.plot(mesh_resolution, r_adapt_error_stress[:,1],'--', color = "green", label = r'$\| e_{\sigma_{VM}}\|_2$'+ ", L-BFGS, r-adapt.")
plt.plot(mesh_resolution, r_adapt_error_u[:,0],':', color = "darkblue", label = r'$\| e_{u_x}\|_2$'+ ", Adam, r-adapt.")
plt.plot(mesh_resolution, r_adapt_error_v[:,0],':', color = "purple", label = r'$\| e_{u_y}\|_2$'+ ", Adam, r-adapt.")
plt.plot(mesh_resolution, r_adapt_error_stress[:,0],':', color = "green", label = r'$\| e_{\sigma_{VM}}\|_2$'+ ", Adam, r-adapt.")
ax.set_xscale('log')
ax.set_yscale('log')
ax.set_ylim([0.00008, 0.3])
plt.xlabel("Number of mesh nodes")
plt.ylabel("Normalized displacement error")
plt.legend(loc='center left', bbox_to_anchor=(1, 0.5),frameon=False )
plt.show()
# Plot maximal stress
fig = matplotlib.pyplot.gcf()
ax = plt.gca()
plt.plot(mesh_resolution, error_stress_max[:,0],'-', color = "purple", label = "Adam")
plt.plot(mesh_resolution, r_adapt_error_stress_max[:,0],':', color = "purple", label = "Adam, r-adaptivity")
plt.plot(mesh_resolution, error_stress_max[:,1],'-', color = "teal", label = "L-BFGS")
plt.plot(mesh_resolution, r_adapt_error_stress_max[:,1],':', color = "teal", label = "L-BFGS, r-adaptivity")
ax.set_xscale('log')
ax.set_yscale('log')
plt.xlabel("Number of mesh nodes")
plt.ylabel(r'$\sigma^{max}_{VM}$')
plt.legend(loc='center left', bbox_to_anchor=(1, 0.5),frameon=False )
plt.show()
# Prerequisites
mesh_resolution = [44, 144, 484, 1804, 7088 ]
# eval_coord_file = "../2D_example/eval_coordinates.npy"
# num_displ_file = "../2D_example/num_solution/num_displacement.npy"
# num_VM_stress_file = "../2D_example/num_solution/num_VM_stress.npy"
eval_coord_file = "GroundTruth/eval_coordinates.npy"
num_displ_file = "GroundTruth/num_displacement.npy"
num_VM_stress_file = "GroundTruth/num_VM_stress.npy"
eval_coord = torch.tensor(numpy.load(eval_coord_file), dtype=torch.float64, requires_grad=True)
num_displ = torch.tensor(numpy.load(num_displ_file))
num_VM_stress = torch.tensor(numpy.load(num_VM_stress_file))
# Experiment
element_size = 2.0
config["solver"]["FrozenMesh"] = False
optimizer = "lbfgs"
refinment = [1,2,3,4,5]
config["interpolation"]["MaxElemSize2D"] = element_size
config["training"]["optimizer"] = optimizer
config["training"]["h_adapt_MaxGeneration"] = 2
rh_adapt_error_u = numpy.zeros((len(refinment)))
rh_adapt_error_v = numpy.zeros((len(refinment)))
rh_adapt_error_stress = numpy.zeros((len(refinment)))
rh_adapt_error_stress_max = numpy.zeros((len(refinment)))
for e in range(len(refinment)):
config["training"]["multiscl_max_refinment"] = refinment[e]
Mat = pre.Material( flag_lame = True, # If True should input lmbda and mu instead of E and nu
coef1 = config["material"]["lmbda"], # Young Modulus
coef2 = config["material"]["mu"] # Poisson's ratio
)
MaxElemSize = pre.ElementSize(
dimension = config["interpolation"]["dimension"],
L = config["geometry"]["L"],
order = config["interpolation"]["order"],
np = config["interpolation"]["np"],
MaxElemSize2D = config["interpolation"]["MaxElemSize2D"]
)
Excluded = []
Mesh_object = pre.Mesh(
config["geometry"]["Name"], # Create the mesh object
MaxElemSize,
config["interpolation"]["order"],
config["interpolation"]["dimension"]
)
Mesh_object.AddBorders(config["Borders"]["Borders"])
Mesh_object.AddBCs( # Include Boundary physical domains infos (BCs+volume)
config["geometry"]["Volume_element"],
Excluded,
config["DirichletDictionryList"]
)
Mesh_object.MeshGeo() # Mesh the .geo file if .msh does not exist
Mesh_object.ReadMesh() # Parse the .msh file
Mesh_object.ExportMeshVtk()
if int(Mesh_object.dim) != int(Mesh_object.dimension):
raise ValueError("The dimension of the provided geometry does not match the job dimension")
Model_FEM = MeshNN_2D(Mesh_object, n_components = 2)
Model_FEM.Freeze_Mesh()
Model_FEM.UnFreeze_FEM()
if not config["solver"]["FrozenMesh"]:
Model_FEM.UnFreeze_Mesh()
Model_FEM.RefinementParameters( MaxGeneration = config["training"]["h_adapt_MaxGeneration"],
Jacobian_threshold = config["training"]["h_adapt_J_thrshld"])
Model_FEM.TrainingParameters( loss_decrease_c = config["training"]["loss_decrease_c"],
Max_epochs = config["training"]["n_epochs"],
learning_rate = config["training"]["learning_rate"])
Model_FEM = Training_2D_FEM(Model_FEM, config, Mat)
# evaluation
Model_FEM.eval()
# Model_FEM.mesh.Nodes = [[i+1,Model_FEM.coordinates[i][0][0].item(),Model_FEM.coordinates[i][0][1].item(),0] for i in range(len(Model_FEM.coordinates))]
coordinates_all = torch.ones_like(Model_FEM.coordinates_all)
coordinates_all[Model_FEM.coord_free] = Model_FEM.coordinates['free']
coordinates_all[~Model_FEM.coord_free] = Model_FEM.coordinates['imposed']
Nodes = torch.hstack([torch.linspace(1,coordinates_all.shape[0],coordinates_all.shape[0], dtype = coordinates_all.dtype, device = coordinates_all.device)[:,None],
coordinates_all])
Nodes = torch.hstack([Nodes,torch.zeros(Nodes.shape[0],1, dtype = Nodes.dtype, device = Nodes.device)])
Model_FEM.mesh.Nodes = Nodes.detach().cpu().numpy()
Model_FEM.mesh.Connectivity = Model_FEM.connectivity
Model_FEM.mesh.ExportMeshVtk(flag_update = True)
elem_IDs = torch.tensor(Model_FEM.mesh.GetCellIds(eval_coord),dtype=torch.int)
u = Model_FEM(eval_coord, elem_IDs)
eps = Strain(u,eval_coord)
sigma = torch.stack(Stress(eps[:,0], eps[:,1], eps[:,2], Mat.lmbda, Mat.mu),dim=1)
sigma_VM = VonMises_plain_strain(sigma, Mat.lmbda, Mat.mu)
rh_adapt_error_u[e] = (torch.linalg.vector_norm(num_displ[:,0] - u[0,:])/torch.linalg.vector_norm(num_displ[:,0])).item()
rh_adapt_error_v[e] = (torch.linalg.vector_norm(num_displ[:,1] - u[1,:])/torch.linalg.vector_norm(num_displ[:,1])).item()
rh_adapt_error_stress[e] = (torch.linalg.vector_norm(num_VM_stress - sigma_VM)/torch.linalg.vector_norm(num_VM_stress)).item()
rh_adapt_error_stress_max[e] = (max(sigma_VM)/max(num_VM_stress)).item()
_ _ ____ ___ __ __
| \ | | ___ _ _| _ \ / _ \| \/ |
| \| |/ _ \ | | | |_) | | | | |\/| |
| |\ | __/ |_| | _ <| |_| | | | |
|_| \_|\___|\__,_|_| \_\ ___/|_| |_|
2024.09.18
************ MESH READING COMPLETE ************
* Dimension of the problem: 2D
* Elements type: t3: 3-node triangle
* Number of Dofs: 88
* Refinement level: 0
**************** START TRAINING ***************
epoch 2 loss = 9.4093e-01
epoch 3 loss = 9.3267e-01
epoch 4 loss = 9.3022e-01
epoch 5 loss = 9.3013e-01
epoch 6 loss = 9.2284e-01
epoch 7 loss = 9.2015e-01
epoch 8 loss = 9.1919e-01
epoch 9 loss = 9.1822e-01
epoch 10 loss = 9.1781e-01
epoch 11 loss = 9.1773e-01
epoch 12 loss = 9.1767e-01
epoch 13 loss = 9.1766e-01
epoch 14 loss = 9.1766e-01
epoch 15 loss = 9.1766e-01
epoch 16 loss = 9.1766e-01
epoch 17 loss = 9.1766e-01
epoch 18 loss = 9.1766e-01
*************** END FIRST PHASE ***************
* Training time: 0.66469407081604s
* Average epoch time: 0.03692744837866889s
***************** END TRAINING ****************
* Training time: 0.66469407081604s
_ _ ____ ___ __ __
| \ | | ___ _ _| _ \ / _ \| \/ |
| \| |/ _ \ | | | |_) | | | | |\/| |
| |\ | __/ |_| | _ <| |_| | | | |
|_| \_|\___|\__,_|_| \_\ ___/|_| |_|
2024.09.18
************ MESH READING COMPLETE ************
* Dimension of the problem: 2D
* Elements type: t3: 3-node triangle
* Number of Dofs: 88
* Refinement level: 0
**************** START TRAINING ***************
epoch 2 loss = 9.4093e-01
epoch 3 loss = 9.3267e-01
epoch 4 loss = 9.3022e-01
epoch 5 loss = 9.3013e-01
epoch 6 loss = 9.2284e-01
epoch 7 loss = 9.2015e-01
epoch 8 loss = 9.1919e-01
epoch 9 loss = 9.1822e-01
epoch 10 loss = 9.1781e-01
epoch 11 loss = 9.1773e-01
epoch 12 loss = 9.1767e-01
epoch 13 loss = 9.1766e-01
epoch 14 loss = 9.1766e-01
epoch 15 loss = 9.1766e-01
epoch 16 loss = 9.1766e-01
epoch 17 loss = 9.1766e-01
epoch 18 loss = 9.1766e-01
*************** END FIRST PHASE ***************
* Training time: 0.6505143642425537s
* Average epoch time: 0.0361396869023641s
************ MESH READING COMPLETE ************
* Dimension of the problem: 2D
* Elements type: t3: 3-node triangle
* Number of Dofs: 288
* Refinement level: 1
**************** START TRAINING ***************
epoch 2 loss = 8.6951e-01
epoch 3 loss = 8.6357e-01
epoch 4 loss = 8.6258e-01
epoch 5 loss = 8.6034e-01
epoch 6 loss = 8.5937e-01
epoch 7 loss = 8.5907e-01
epoch 8 loss = 8.5867e-01
epoch 9 loss = 8.5831e-01
epoch 10 loss = 8.5799e-01
epoch 11 loss = 8.5773e-01
epoch 12 loss = 8.5751e-01
epoch 13 loss = 8.5721e-01
epoch 14 loss = 8.5682e-01
epoch 15 loss = 8.5671e-01
epoch 16 loss = 8.5660e-01
epoch 17 loss = 8.5649e-01
epoch 18 loss = 8.5642e-01
epoch 19 loss = 8.5617e-01
epoch 20 loss = 8.5574e-01
epoch 21 loss = 8.5540e-01
epoch 22 loss = 8.5509e-01
epoch 23 loss = 8.5496e-01
epoch 24 loss = 8.5481e-01
epoch 25 loss = 8.5471e-01
epoch 26 loss = 8.5462e-01
epoch 27 loss = 8.5453e-01
epoch 28 loss = 8.5449e-01
epoch 29 loss = 8.5440e-01
epoch 30 loss = 8.5432e-01
epoch 31 loss = 8.5425e-01
epoch 32 loss = 8.5420e-01
epoch 33 loss = 8.5417e-01
epoch 34 loss = 8.5415e-01
epoch 35 loss = 8.5413e-01
epoch 36 loss = 8.5408e-01
epoch 37 loss = 8.5405e-01
epoch 38 loss = 8.5399e-01
epoch 39 loss = 8.5393e-01
epoch 40 loss = 8.5389e-01
epoch 41 loss = 8.5387e-01
epoch 42 loss = 8.5385e-01
epoch 43 loss = 8.5382e-01
epoch 44 loss = 8.5380e-01
epoch 45 loss = 8.5378e-01
epoch 46 loss = 8.5375e-01
epoch 47 loss = 8.5372e-01
epoch 48 loss = 8.5369e-01
epoch 49 loss = 8.5367e-01
epoch 50 loss = 8.5364e-01
epoch 51 loss = 8.5358e-01
epoch 52 loss = 8.5351e-01
epoch 53 loss = 8.5347e-01
epoch 54 loss = 8.5344e-01
epoch 55 loss = 8.5338e-01
epoch 56 loss = 8.5337e-01
epoch 57 loss = 8.5316e-01
epoch 58 loss = 8.5309e-01
epoch 59 loss = 8.5297e-01
epoch 60 loss = 8.5295e-01
epoch 61 loss = 8.5290e-01
epoch 62 loss = 8.5286e-01
epoch 63 loss = 8.5283e-01
epoch 64 loss = 8.5281e-01
epoch 65 loss = 8.5277e-01
epoch 66 loss = 8.5276e-01
epoch 67 loss = 8.5274e-01
epoch 68 loss = 8.5263e-01
epoch 69 loss = 8.5259e-01
epoch 70 loss = 8.5254e-01
epoch 71 loss = 8.5251e-01
epoch 72 loss = 8.5249e-01
epoch 73 loss = 8.5247e-01
epoch 74 loss = 8.5246e-01
epoch 75 loss = 8.5244e-01
epoch 76 loss = 8.5242e-01
epoch 77 loss = 8.5241e-01
epoch 78 loss = 8.5240e-01
epoch 79 loss = 8.5239e-01
epoch 80 loss = 8.5238e-01
epoch 81 loss = 8.5238e-01
epoch 82 loss = 8.5237e-01
epoch 83 loss = 8.5236e-01
epoch 84 loss = 8.5234e-01
epoch 85 loss = 8.5234e-01
epoch 86 loss = 8.5232e-01
epoch 87 loss = 8.5230e-01
epoch 88 loss = 8.5229e-01
epoch 89 loss = 8.5228e-01
epoch 90 loss = 8.5227e-01
epoch 91 loss = 8.5227e-01
epoch 92 loss = 8.5225e-01
epoch 93 loss = 8.5224e-01
epoch 94 loss = 8.5224e-01
epoch 95 loss = 8.5223e-01
epoch 96 loss = 8.5222e-01
epoch 97 loss = 8.5221e-01
epoch 98 loss = 8.5220e-01
epoch 99 loss = 8.5219e-01
epoch 100 loss = 8.5216e-01
epoch 101 loss = 8.5213e-01
epoch 102 loss = 8.5210e-01
epoch 103 loss = 8.5209e-01
epoch 104 loss = 8.5208e-01
epoch 105 loss = 8.5208e-01
epoch 106 loss = 8.5207e-01
epoch 107 loss = 8.5207e-01
epoch 108 loss = 8.5206e-01
epoch 109 loss = 8.5205e-01
epoch 110 loss = 8.5205e-01
epoch 111 loss = 8.5204e-01
epoch 112 loss = 8.5203e-01
epoch 113 loss = 8.5203e-01
epoch 114 loss = 8.5202e-01
epoch 115 loss = 8.5201e-01
epoch 116 loss = 8.5200e-01
epoch 117 loss = 8.5199e-01
epoch 118 loss = 8.5199e-01
epoch 119 loss = 8.5198e-01
epoch 120 loss = 8.5198e-01
epoch 121 loss = 8.5197e-01
epoch 122 loss = 8.5197e-01
epoch 123 loss = 8.5197e-01
epoch 124 loss = 8.5196e-01
epoch 125 loss = 8.5196e-01
epoch 126 loss = 8.5195e-01
epoch 127 loss = 8.5195e-01
epoch 128 loss = 8.5194e-01
epoch 129 loss = 8.5193e-01
epoch 130 loss = 8.5193e-01
epoch 131 loss = 8.5193e-01
epoch 132 loss = 8.5192e-01
epoch 133 loss = 8.5192e-01
epoch 134 loss = 8.5192e-01
epoch 135 loss = 8.5192e-01
epoch 136 loss = 8.5191e-01
epoch 137 loss = 8.5191e-01
epoch 138 loss = 8.519e-01
epoch 139 loss = 8.519e-01
epoch 140 loss = 8.5189e-01
epoch 141 loss = 8.5189e-01
epoch 142 loss = 8.5189e-01
epoch 143 loss = 8.5188e-01
epoch 144 loss = 8.5188e-01
epoch 145 loss = 8.5188e-01
epoch 146 loss = 8.5188e-01
epoch 147 loss = 8.5187e-01
epoch 148 loss = 8.5186e-01
epoch 149 loss = 8.5186e-01
epoch 150 loss = 8.5186e-01
epoch 151 loss = 8.5185e-01
epoch 152 loss = 8.5184e-01
epoch 153 loss = 8.5183e-01
epoch 154 loss = 8.5182e-01
epoch 155 loss = 8.5182e-01
epoch 156 loss = 8.5182e-01
epoch 157 loss = 8.5181e-01
epoch 158 loss = 8.5181e-01
epoch 159 loss = 8.5181e-01
epoch 160 loss = 8.5181e-01
epoch 161 loss = 8.5181e-01
epoch 162 loss = 8.5180e-01
epoch 163 loss = 8.518e-01
epoch 164 loss = 8.518e-01
epoch 165 loss = 8.518e-01
epoch 166 loss = 8.518e-01
epoch 167 loss = 8.518e-01
epoch 168 loss = 8.5179e-01
epoch 169 loss = 8.5179e-01
epoch 170 loss = 8.5179e-01
epoch 171 loss = 8.5179e-01
epoch 172 loss = 8.5179e-01
epoch 173 loss = 8.5179e-01
epoch 174 loss = 8.5179e-01
epoch 175 loss = 8.5179e-01
epoch 176 loss = 8.5179e-01
epoch 177 loss = 8.5179e-01
epoch 178 loss = 8.5178e-01
epoch 179 loss = 8.5178e-01
epoch 180 loss = 8.5178e-01
epoch 181 loss = 8.5178e-01
epoch 182 loss = 8.5178e-01
epoch 183 loss = 8.5175e-01
epoch 184 loss = 8.5175e-01
epoch 185 loss = 8.5175e-01
epoch 186 loss = 8.5175e-01
epoch 187 loss = 8.5175e-01
epoch 188 loss = 8.5174e-01
epoch 189 loss = 8.5174e-01
epoch 190 loss = 8.5174e-01
epoch 191 loss = 8.5174e-01
epoch 192 loss = 8.5174e-01
epoch 193 loss = 8.5174e-01
epoch 194 loss = 8.5174e-01
epoch 195 loss = 8.5174e-01
epoch 196 loss = 8.5174e-01
epoch 197 loss = 8.5174e-01
epoch 198 loss = 8.5173e-01
epoch 199 loss = 8.5173e-01
epoch 200 loss = 8.5173e-01
epoch 201 loss = 8.5173e-01
epoch 202 loss = 8.5173e-01
epoch 203 loss = 8.5173e-01
*************** END FIRST PHASE ***************
* Training time: 8.929752826690674s
* Average epoch time: 0.043988930180742236s
***************** END TRAINING ****************
* Training time: 9.580267190933228s
_ _ ____ ___ __ __
| \ | | ___ _ _| _ \ / _ \| \/ |
| \| |/ _ \ | | | |_) | | | | |\/| |
| |\ | __/ |_| | _ <| |_| | | | |
|_| \_|\___|\__,_|_| \_\ ___/|_| |_|
2024.09.18
************ MESH READING COMPLETE ************
* Dimension of the problem: 2D
* Elements type: t3: 3-node triangle
* Number of Dofs: 88
* Refinement level: 0
**************** START TRAINING ***************
epoch 2 loss = 9.4093e-01
epoch 3 loss = 9.3267e-01
epoch 4 loss = 9.3022e-01
epoch 5 loss = 9.3013e-01
epoch 6 loss = 9.2284e-01
epoch 7 loss = 9.2015e-01
epoch 8 loss = 9.1919e-01
epoch 9 loss = 9.1822e-01
epoch 10 loss = 9.1781e-01
epoch 11 loss = 9.1773e-01
epoch 12 loss = 9.1767e-01
epoch 13 loss = 9.1766e-01
epoch 14 loss = 9.1766e-01
epoch 15 loss = 9.1766e-01
epoch 16 loss = 9.1766e-01
epoch 17 loss = 9.1766e-01
epoch 18 loss = 9.1766e-01
*************** END FIRST PHASE ***************
* Training time: 0.6518869400024414s
* Average epoch time: 0.03621594111124674s
************ MESH READING COMPLETE ************
* Dimension of the problem: 2D
* Elements type: t3: 3-node triangle
* Number of Dofs: 288
* Refinement level: 1
**************** START TRAINING ***************
epoch 2 loss = 8.6951e-01
epoch 3 loss = 8.6357e-01
epoch 4 loss = 8.6258e-01
epoch 5 loss = 8.6034e-01
epoch 6 loss = 8.5937e-01
epoch 7 loss = 8.5907e-01
epoch 8 loss = 8.5867e-01
epoch 9 loss = 8.5831e-01
epoch 10 loss = 8.5799e-01
epoch 11 loss = 8.5773e-01
epoch 12 loss = 8.5751e-01
epoch 13 loss = 8.5721e-01
epoch 14 loss = 8.5682e-01
epoch 15 loss = 8.5671e-01
epoch 16 loss = 8.5660e-01
epoch 17 loss = 8.5649e-01
epoch 18 loss = 8.5642e-01
epoch 19 loss = 8.5617e-01
epoch 20 loss = 8.5574e-01
epoch 21 loss = 8.5540e-01
epoch 22 loss = 8.5509e-01
epoch 23 loss = 8.5496e-01
epoch 24 loss = 8.5481e-01
epoch 25 loss = 8.5471e-01
epoch 26 loss = 8.5462e-01
epoch 27 loss = 8.5453e-01
epoch 28 loss = 8.5449e-01
epoch 29 loss = 8.5440e-01
epoch 30 loss = 8.5432e-01
epoch 31 loss = 8.5425e-01
epoch 32 loss = 8.5420e-01
epoch 33 loss = 8.5417e-01
epoch 34 loss = 8.5415e-01
epoch 35 loss = 8.5413e-01
epoch 36 loss = 8.5408e-01
epoch 37 loss = 8.5405e-01
epoch 38 loss = 8.5399e-01
epoch 39 loss = 8.5393e-01
epoch 40 loss = 8.5389e-01
epoch 41 loss = 8.5387e-01
epoch 42 loss = 8.5385e-01
epoch 43 loss = 8.5382e-01
epoch 44 loss = 8.5380e-01
epoch 45 loss = 8.5378e-01
epoch 46 loss = 8.5375e-01
epoch 47 loss = 8.5372e-01
epoch 48 loss = 8.5369e-01
epoch 49 loss = 8.5367e-01
epoch 50 loss = 8.5364e-01
epoch 51 loss = 8.5358e-01
epoch 52 loss = 8.5351e-01
epoch 53 loss = 8.5347e-01
epoch 54 loss = 8.5344e-01
epoch 55 loss = 8.5338e-01
epoch 56 loss = 8.5337e-01
epoch 57 loss = 8.5316e-01
epoch 58 loss = 8.5309e-01
epoch 59 loss = 8.5297e-01
epoch 60 loss = 8.5295e-01
epoch 61 loss = 8.5290e-01
epoch 62 loss = 8.5286e-01
epoch 63 loss = 8.5283e-01
epoch 64 loss = 8.5281e-01
epoch 65 loss = 8.5277e-01
epoch 66 loss = 8.5276e-01
epoch 67 loss = 8.5274e-01
epoch 68 loss = 8.5263e-01
epoch 69 loss = 8.5259e-01
epoch 70 loss = 8.5254e-01
epoch 71 loss = 8.5251e-01
epoch 72 loss = 8.5249e-01
epoch 73 loss = 8.5247e-01
epoch 74 loss = 8.5246e-01
epoch 75 loss = 8.5244e-01
epoch 76 loss = 8.5242e-01
epoch 77 loss = 8.5241e-01
epoch 78 loss = 8.5240e-01
epoch 79 loss = 8.5239e-01
epoch 80 loss = 8.5238e-01
epoch 81 loss = 8.5238e-01
epoch 82 loss = 8.5237e-01
epoch 83 loss = 8.5236e-01
epoch 84 loss = 8.5234e-01
epoch 85 loss = 8.5234e-01
epoch 86 loss = 8.5232e-01
epoch 87 loss = 8.5230e-01
epoch 88 loss = 8.5229e-01
epoch 89 loss = 8.5228e-01
epoch 90 loss = 8.5227e-01
epoch 91 loss = 8.5227e-01
epoch 92 loss = 8.5225e-01
epoch 93 loss = 8.5224e-01
epoch 94 loss = 8.5224e-01
epoch 95 loss = 8.5223e-01
epoch 96 loss = 8.5222e-01
epoch 97 loss = 8.5221e-01
epoch 98 loss = 8.5220e-01
epoch 99 loss = 8.5219e-01
epoch 100 loss = 8.5216e-01
epoch 101 loss = 8.5213e-01
epoch 102 loss = 8.5210e-01
epoch 103 loss = 8.5209e-01
epoch 104 loss = 8.5208e-01
epoch 105 loss = 8.5208e-01
epoch 106 loss = 8.5207e-01
epoch 107 loss = 8.5207e-01
epoch 108 loss = 8.5206e-01
epoch 109 loss = 8.5205e-01
epoch 110 loss = 8.5205e-01
epoch 111 loss = 8.5204e-01
epoch 112 loss = 8.5203e-01
epoch 113 loss = 8.5203e-01
epoch 114 loss = 8.5202e-01
epoch 115 loss = 8.5201e-01
epoch 116 loss = 8.5200e-01
epoch 117 loss = 8.5199e-01
epoch 118 loss = 8.5199e-01
epoch 119 loss = 8.5198e-01
epoch 120 loss = 8.5198e-01
epoch 121 loss = 8.5197e-01
epoch 122 loss = 8.5197e-01
epoch 123 loss = 8.5197e-01
epoch 124 loss = 8.5196e-01
epoch 125 loss = 8.5196e-01
epoch 126 loss = 8.5195e-01
epoch 127 loss = 8.5195e-01
epoch 128 loss = 8.5194e-01
epoch 129 loss = 8.5193e-01
epoch 130 loss = 8.5193e-01
epoch 131 loss = 8.5193e-01
epoch 132 loss = 8.5192e-01
epoch 133 loss = 8.5192e-01
epoch 134 loss = 8.5192e-01
epoch 135 loss = 8.5192e-01
epoch 136 loss = 8.5191e-01
epoch 137 loss = 8.5191e-01
epoch 138 loss = 8.519e-01
epoch 139 loss = 8.519e-01
epoch 140 loss = 8.5189e-01
epoch 141 loss = 8.5189e-01
epoch 142 loss = 8.5189e-01
epoch 143 loss = 8.5188e-01
epoch 144 loss = 8.5188e-01
epoch 145 loss = 8.5188e-01
epoch 146 loss = 8.5188e-01
epoch 147 loss = 8.5187e-01
epoch 148 loss = 8.5186e-01
epoch 149 loss = 8.5186e-01
epoch 150 loss = 8.5186e-01
epoch 151 loss = 8.5185e-01
epoch 152 loss = 8.5184e-01
epoch 153 loss = 8.5183e-01
epoch 154 loss = 8.5182e-01
epoch 155 loss = 8.5182e-01
epoch 156 loss = 8.5182e-01
epoch 157 loss = 8.5181e-01
epoch 158 loss = 8.5181e-01
epoch 159 loss = 8.5181e-01
epoch 160 loss = 8.5181e-01
epoch 161 loss = 8.5181e-01
epoch 162 loss = 8.5180e-01
epoch 163 loss = 8.518e-01
epoch 164 loss = 8.518e-01
epoch 165 loss = 8.518e-01
epoch 166 loss = 8.518e-01
epoch 167 loss = 8.518e-01
epoch 168 loss = 8.5179e-01
epoch 169 loss = 8.5179e-01
epoch 170 loss = 8.5179e-01
epoch 171 loss = 8.5179e-01
epoch 172 loss = 8.5179e-01
epoch 173 loss = 8.5179e-01
epoch 174 loss = 8.5179e-01
epoch 175 loss = 8.5179e-01
epoch 176 loss = 8.5179e-01
epoch 177 loss = 8.5179e-01
epoch 178 loss = 8.5178e-01
epoch 179 loss = 8.5178e-01
epoch 180 loss = 8.5178e-01
epoch 181 loss = 8.5178e-01
epoch 182 loss = 8.5178e-01
epoch 183 loss = 8.5175e-01
epoch 184 loss = 8.5175e-01
epoch 185 loss = 8.5175e-01
epoch 186 loss = 8.5175e-01
epoch 187 loss = 8.5175e-01
epoch 188 loss = 8.5174e-01
epoch 189 loss = 8.5174e-01
epoch 190 loss = 8.5174e-01
epoch 191 loss = 8.5174e-01
epoch 192 loss = 8.5174e-01
epoch 193 loss = 8.5174e-01
epoch 194 loss = 8.5174e-01
epoch 195 loss = 8.5174e-01
epoch 196 loss = 8.5174e-01
epoch 197 loss = 8.5174e-01
epoch 198 loss = 8.5173e-01
epoch 199 loss = 8.5173e-01
epoch 200 loss = 8.5173e-01
epoch 201 loss = 8.5173e-01
epoch 202 loss = 8.5173e-01
epoch 203 loss = 8.5173e-01
*************** END FIRST PHASE ***************
* Training time: 8.922935724258423s
* Average epoch time: 0.043955348395361686s
************ MESH READING COMPLETE ************
* Dimension of the problem: 2D
* Elements type: t3: 3-node triangle
* Number of Dofs: 968
* Refinement level: 2
**************** START TRAINING ***************
epoch 2 loss = 8.416e-01
epoch 3 loss = 8.4073e-01
epoch 4 loss = 8.4028e-01
epoch 5 loss = 8.4001e-01
epoch 6 loss = 8.3982e-01
epoch 7 loss = 8.3939e-01
epoch 8 loss = 8.3927e-01
epoch 9 loss = 8.3907e-01
epoch 10 loss = 8.3898e-01
epoch 11 loss = 8.3876e-01
epoch 12 loss = 8.3867e-01
epoch 13 loss = 8.3855e-01
epoch 14 loss = 8.3846e-01
epoch 15 loss = 8.3843e-01
epoch 16 loss = 8.3834e-01
epoch 17 loss = 8.3829e-01
epoch 18 loss = 8.3822e-01
epoch 19 loss = 8.3815e-01
epoch 20 loss = 8.3809e-01
epoch 21 loss = 8.3802e-01
epoch 22 loss = 8.3798e-01
epoch 23 loss = 8.3793e-01
epoch 24 loss = 8.3789e-01
epoch 25 loss = 8.3788e-01
epoch 26 loss = 8.3784e-01
epoch 27 loss = 8.3781e-01
epoch 28 loss = 8.3777e-01
epoch 29 loss = 8.3774e-01
epoch 30 loss = 8.3771e-01
epoch 31 loss = 8.3768e-01
epoch 32 loss = 8.3763e-01
epoch 33 loss = 8.3759e-01
epoch 34 loss = 8.3756e-01
epoch 35 loss = 8.3754e-01
epoch 36 loss = 8.3752e-01
epoch 37 loss = 8.3750e-01
epoch 38 loss = 8.3748e-01
epoch 39 loss = 8.3746e-01
epoch 40 loss = 8.3746e-01
epoch 41 loss = 8.3744e-01
epoch 42 loss = 8.3743e-01
epoch 43 loss = 8.3742e-01
epoch 44 loss = 8.3741e-01
epoch 45 loss = 8.3739e-01
epoch 46 loss = 8.3737e-01
epoch 47 loss = 8.3736e-01
epoch 48 loss = 8.3734e-01
epoch 49 loss = 8.3733e-01
epoch 50 loss = 8.3732e-01
epoch 51 loss = 8.3731e-01
epoch 52 loss = 8.373e-01
epoch 53 loss = 8.3728e-01
epoch 54 loss = 8.3726e-01
epoch 55 loss = 8.3726e-01
epoch 56 loss = 8.3724e-01
epoch 57 loss = 8.3723e-01
epoch 58 loss = 8.3717e-01
epoch 59 loss = 8.3716e-01
epoch 60 loss = 8.3715e-01
epoch 61 loss = 8.3713e-01
epoch 62 loss = 8.3711e-01
epoch 63 loss = 8.371e-01
epoch 64 loss = 8.3709e-01
epoch 65 loss = 8.3709e-01
epoch 66 loss = 8.3709e-01
epoch 67 loss = 8.3707e-01
epoch 68 loss = 8.3707e-01
epoch 69 loss = 8.3703e-01
epoch 70 loss = 8.3703e-01
epoch 71 loss = 8.3703e-01
epoch 72 loss = 8.3703e-01
epoch 73 loss = 8.3702e-01
epoch 74 loss = 8.3701e-01
epoch 75 loss = 8.3701e-01
epoch 76 loss = 8.3698e-01
epoch 77 loss = 8.3698e-01
epoch 78 loss = 8.3698e-01
epoch 79 loss = 8.3697e-01
epoch 80 loss = 8.3697e-01
epoch 81 loss = 8.3696e-01
epoch 82 loss = 8.3695e-01
epoch 83 loss = 8.3695e-01
epoch 84 loss = 8.3694e-01
epoch 85 loss = 8.3693e-01
epoch 86 loss = 8.3693e-01
epoch 87 loss = 8.3693e-01
epoch 88 loss = 8.3692e-01
epoch 89 loss = 8.3692e-01
epoch 90 loss = 8.3691e-01
epoch 91 loss = 8.369e-01
epoch 92 loss = 8.3687e-01
epoch 93 loss = 8.3686e-01
epoch 94 loss = 8.3686e-01
epoch 95 loss = 8.3685e-01
epoch 96 loss = 8.3684e-01
epoch 97 loss = 8.3683e-01
epoch 98 loss = 8.3683e-01
epoch 99 loss = 8.3682e-01
epoch 100 loss = 8.3682e-01
epoch 101 loss = 8.3682e-01
epoch 102 loss = 8.3682e-01
epoch 103 loss = 8.3681e-01
epoch 104 loss = 8.3681e-01
epoch 105 loss = 8.368e-01
epoch 106 loss = 8.3679e-01
epoch 107 loss = 8.3679e-01
epoch 108 loss = 8.3679e-01
epoch 109 loss = 8.3678e-01
epoch 110 loss = 8.3678e-01
epoch 111 loss = 8.3677e-01
epoch 112 loss = 8.3676e-01
epoch 113 loss = 8.3676e-01
epoch 114 loss = 8.3676e-01
epoch 115 loss = 8.3676e-01
epoch 116 loss = 8.3676e-01
epoch 117 loss = 8.3676e-01
*************** END FIRST PHASE ***************
* Training time: 5.651291131973267s
* Average epoch time: 0.048301633606609114s
***************** END TRAINING ****************
* Training time: 15.22611379623413s
_ _ ____ ___ __ __
| \ | | ___ _ _| _ \ / _ \| \/ |
| \| |/ _ \ | | | |_) | | | | |\/| |
| |\ | __/ |_| | _ <| |_| | | | |
|_| \_|\___|\__,_|_| \_\ ___/|_| |_|
2024.09.18
************ MESH READING COMPLETE ************
* Dimension of the problem: 2D
* Elements type: t3: 3-node triangle
* Number of Dofs: 88
* Refinement level: 0
**************** START TRAINING ***************
epoch 2 loss = 9.4093e-01
epoch 3 loss = 9.3267e-01
epoch 4 loss = 9.3022e-01
epoch 5 loss = 9.3013e-01
epoch 6 loss = 9.2284e-01
epoch 7 loss = 9.2015e-01
epoch 8 loss = 9.1919e-01
epoch 9 loss = 9.1822e-01
epoch 10 loss = 9.1781e-01
epoch 11 loss = 9.1773e-01
epoch 12 loss = 9.1767e-01
epoch 13 loss = 9.1766e-01
epoch 14 loss = 9.1766e-01
epoch 15 loss = 9.1766e-01
epoch 16 loss = 9.1766e-01
epoch 17 loss = 9.1766e-01
epoch 18 loss = 9.1766e-01
*************** END FIRST PHASE ***************
* Training time: 0.64969801902771s
* Average epoch time: 0.036094334390428334s
************ MESH READING COMPLETE ************
* Dimension of the problem: 2D
* Elements type: t3: 3-node triangle
* Number of Dofs: 288
* Refinement level: 1
**************** START TRAINING ***************
epoch 2 loss = 8.6951e-01
epoch 3 loss = 8.6357e-01
epoch 4 loss = 8.6258e-01
epoch 5 loss = 8.6034e-01
epoch 6 loss = 8.5937e-01
epoch 7 loss = 8.5907e-01
epoch 8 loss = 8.5867e-01
epoch 9 loss = 8.5831e-01
epoch 10 loss = 8.5799e-01
epoch 11 loss = 8.5773e-01
epoch 12 loss = 8.5751e-01
epoch 13 loss = 8.5721e-01
epoch 14 loss = 8.5682e-01
epoch 15 loss = 8.5671e-01
epoch 16 loss = 8.5660e-01
epoch 17 loss = 8.5649e-01
epoch 18 loss = 8.5642e-01
epoch 19 loss = 8.5617e-01
epoch 20 loss = 8.5574e-01
epoch 21 loss = 8.5540e-01
epoch 22 loss = 8.5509e-01
epoch 23 loss = 8.5496e-01
epoch 24 loss = 8.5481e-01
epoch 25 loss = 8.5471e-01
epoch 26 loss = 8.5462e-01
epoch 27 loss = 8.5453e-01
epoch 28 loss = 8.5449e-01
epoch 29 loss = 8.5440e-01
epoch 30 loss = 8.5432e-01
epoch 31 loss = 8.5425e-01
epoch 32 loss = 8.5420e-01
epoch 33 loss = 8.5417e-01
epoch 34 loss = 8.5415e-01
epoch 35 loss = 8.5413e-01
epoch 36 loss = 8.5408e-01
epoch 37 loss = 8.5405e-01
epoch 38 loss = 8.5399e-01
epoch 39 loss = 8.5393e-01
epoch 40 loss = 8.5389e-01
epoch 41 loss = 8.5387e-01
epoch 42 loss = 8.5385e-01
epoch 43 loss = 8.5382e-01
epoch 44 loss = 8.5380e-01
epoch 45 loss = 8.5378e-01
epoch 46 loss = 8.5375e-01
epoch 47 loss = 8.5372e-01
epoch 48 loss = 8.5369e-01
epoch 49 loss = 8.5367e-01
epoch 50 loss = 8.5364e-01
epoch 51 loss = 8.5358e-01
epoch 52 loss = 8.5351e-01
epoch 53 loss = 8.5347e-01
epoch 54 loss = 8.5344e-01
epoch 55 loss = 8.5338e-01
epoch 56 loss = 8.5337e-01
epoch 57 loss = 8.5316e-01
epoch 58 loss = 8.5309e-01
epoch 59 loss = 8.5297e-01
epoch 60 loss = 8.5295e-01
epoch 61 loss = 8.5290e-01
epoch 62 loss = 8.5286e-01
epoch 63 loss = 8.5283e-01
epoch 64 loss = 8.5281e-01
epoch 65 loss = 8.5277e-01
epoch 66 loss = 8.5276e-01
epoch 67 loss = 8.5274e-01
epoch 68 loss = 8.5263e-01
epoch 69 loss = 8.5259e-01
epoch 70 loss = 8.5254e-01
epoch 71 loss = 8.5251e-01
epoch 72 loss = 8.5249e-01
epoch 73 loss = 8.5247e-01
epoch 74 loss = 8.5246e-01
epoch 75 loss = 8.5244e-01
epoch 76 loss = 8.5242e-01
epoch 77 loss = 8.5241e-01
epoch 78 loss = 8.5240e-01
epoch 79 loss = 8.5239e-01
epoch 80 loss = 8.5238e-01
epoch 81 loss = 8.5238e-01
epoch 82 loss = 8.5237e-01
epoch 83 loss = 8.5236e-01
epoch 84 loss = 8.5234e-01
epoch 85 loss = 8.5234e-01
epoch 86 loss = 8.5232e-01
epoch 87 loss = 8.5230e-01
epoch 88 loss = 8.5229e-01
epoch 89 loss = 8.5228e-01
epoch 90 loss = 8.5227e-01
epoch 91 loss = 8.5227e-01
epoch 92 loss = 8.5225e-01
epoch 93 loss = 8.5224e-01
epoch 94 loss = 8.5224e-01
epoch 95 loss = 8.5223e-01
epoch 96 loss = 8.5222e-01
epoch 97 loss = 8.5221e-01
epoch 98 loss = 8.5220e-01
epoch 99 loss = 8.5219e-01
epoch 100 loss = 8.5216e-01
epoch 101 loss = 8.5213e-01
epoch 102 loss = 8.5210e-01
epoch 103 loss = 8.5209e-01
epoch 104 loss = 8.5208e-01
epoch 105 loss = 8.5208e-01
epoch 106 loss = 8.5207e-01
epoch 107 loss = 8.5207e-01
epoch 108 loss = 8.5206e-01
epoch 109 loss = 8.5205e-01
epoch 110 loss = 8.5205e-01
epoch 111 loss = 8.5204e-01
epoch 112 loss = 8.5203e-01
epoch 113 loss = 8.5203e-01
epoch 114 loss = 8.5202e-01
epoch 115 loss = 8.5201e-01
epoch 116 loss = 8.5200e-01
epoch 117 loss = 8.5199e-01
epoch 118 loss = 8.5199e-01
epoch 119 loss = 8.5198e-01
epoch 120 loss = 8.5198e-01
epoch 121 loss = 8.5197e-01
epoch 122 loss = 8.5197e-01
epoch 123 loss = 8.5197e-01
epoch 124 loss = 8.5196e-01
epoch 125 loss = 8.5196e-01
epoch 126 loss = 8.5195e-01
epoch 127 loss = 8.5195e-01
epoch 128 loss = 8.5194e-01
epoch 129 loss = 8.5193e-01
epoch 130 loss = 8.5193e-01
epoch 131 loss = 8.5193e-01
epoch 132 loss = 8.5192e-01
epoch 133 loss = 8.5192e-01
epoch 134 loss = 8.5192e-01
epoch 135 loss = 8.5192e-01
epoch 136 loss = 8.5191e-01
epoch 137 loss = 8.5191e-01
epoch 138 loss = 8.519e-01
epoch 139 loss = 8.519e-01
epoch 140 loss = 8.5189e-01
epoch 141 loss = 8.5189e-01
epoch 142 loss = 8.5189e-01
epoch 143 loss = 8.5188e-01
epoch 144 loss = 8.5188e-01
epoch 145 loss = 8.5188e-01
epoch 146 loss = 8.5188e-01
epoch 147 loss = 8.5187e-01
epoch 148 loss = 8.5186e-01
epoch 149 loss = 8.5186e-01
epoch 150 loss = 8.5186e-01
epoch 151 loss = 8.5185e-01
epoch 152 loss = 8.5184e-01
epoch 153 loss = 8.5183e-01
epoch 154 loss = 8.5182e-01
epoch 155 loss = 8.5182e-01
epoch 156 loss = 8.5182e-01
epoch 157 loss = 8.5181e-01
epoch 158 loss = 8.5181e-01
epoch 159 loss = 8.5181e-01
epoch 160 loss = 8.5181e-01
epoch 161 loss = 8.5181e-01
epoch 162 loss = 8.5180e-01
epoch 163 loss = 8.518e-01
epoch 164 loss = 8.518e-01
epoch 165 loss = 8.518e-01
epoch 166 loss = 8.518e-01
epoch 167 loss = 8.518e-01
epoch 168 loss = 8.5179e-01
epoch 169 loss = 8.5179e-01
epoch 170 loss = 8.5179e-01
epoch 171 loss = 8.5179e-01
epoch 172 loss = 8.5179e-01
epoch 173 loss = 8.5179e-01
epoch 174 loss = 8.5179e-01
epoch 175 loss = 8.5179e-01
epoch 176 loss = 8.5179e-01
epoch 177 loss = 8.5179e-01
epoch 178 loss = 8.5178e-01
epoch 179 loss = 8.5178e-01
epoch 180 loss = 8.5178e-01
epoch 181 loss = 8.5178e-01
epoch 182 loss = 8.5178e-01
epoch 183 loss = 8.5175e-01
epoch 184 loss = 8.5175e-01
epoch 185 loss = 8.5175e-01
epoch 186 loss = 8.5175e-01
epoch 187 loss = 8.5175e-01
epoch 188 loss = 8.5174e-01
epoch 189 loss = 8.5174e-01
epoch 190 loss = 8.5174e-01
epoch 191 loss = 8.5174e-01
epoch 192 loss = 8.5174e-01
epoch 193 loss = 8.5174e-01
epoch 194 loss = 8.5174e-01
epoch 195 loss = 8.5174e-01
epoch 196 loss = 8.5174e-01
epoch 197 loss = 8.5174e-01
epoch 198 loss = 8.5173e-01
epoch 199 loss = 8.5173e-01
epoch 200 loss = 8.5173e-01
epoch 201 loss = 8.5173e-01
epoch 202 loss = 8.5173e-01
epoch 203 loss = 8.5173e-01
*************** END FIRST PHASE ***************
* Training time: 8.92120099067688s
* Average epoch time: 0.043946802909738326s
************ MESH READING COMPLETE ************
* Dimension of the problem: 2D
* Elements type: t3: 3-node triangle
* Number of Dofs: 968
* Refinement level: 2
**************** START TRAINING ***************
epoch 2 loss = 8.416e-01
epoch 3 loss = 8.4073e-01
epoch 4 loss = 8.4028e-01
epoch 5 loss = 8.4001e-01
epoch 6 loss = 8.3982e-01
epoch 7 loss = 8.3939e-01
epoch 8 loss = 8.3927e-01
epoch 9 loss = 8.3907e-01
epoch 10 loss = 8.3898e-01
epoch 11 loss = 8.3876e-01
epoch 12 loss = 8.3867e-01
epoch 13 loss = 8.3855e-01
epoch 14 loss = 8.3846e-01
epoch 15 loss = 8.3843e-01
epoch 16 loss = 8.3834e-01
epoch 17 loss = 8.3829e-01
epoch 18 loss = 8.3822e-01
epoch 19 loss = 8.3815e-01
epoch 20 loss = 8.3809e-01
epoch 21 loss = 8.3802e-01
epoch 22 loss = 8.3798e-01
epoch 23 loss = 8.3793e-01
epoch 24 loss = 8.3789e-01
epoch 25 loss = 8.3788e-01
epoch 26 loss = 8.3784e-01
epoch 27 loss = 8.3781e-01
epoch 28 loss = 8.3777e-01
epoch 29 loss = 8.3774e-01
epoch 30 loss = 8.3771e-01
epoch 31 loss = 8.3768e-01
epoch 32 loss = 8.3763e-01
epoch 33 loss = 8.3759e-01
epoch 34 loss = 8.3756e-01
epoch 35 loss = 8.3754e-01
epoch 36 loss = 8.3752e-01
epoch 37 loss = 8.3750e-01
epoch 38 loss = 8.3748e-01
epoch 39 loss = 8.3746e-01
epoch 40 loss = 8.3746e-01
epoch 41 loss = 8.3744e-01
epoch 42 loss = 8.3743e-01
epoch 43 loss = 8.3742e-01
epoch 44 loss = 8.3741e-01
epoch 45 loss = 8.3739e-01
epoch 46 loss = 8.3737e-01
epoch 47 loss = 8.3736e-01
epoch 48 loss = 8.3734e-01
epoch 49 loss = 8.3733e-01
epoch 50 loss = 8.3732e-01
epoch 51 loss = 8.3731e-01
epoch 52 loss = 8.373e-01
epoch 53 loss = 8.3728e-01
epoch 54 loss = 8.3726e-01
epoch 55 loss = 8.3726e-01
epoch 56 loss = 8.3724e-01
epoch 57 loss = 8.3723e-01
epoch 58 loss = 8.3717e-01
epoch 59 loss = 8.3716e-01
epoch 60 loss = 8.3715e-01
epoch 61 loss = 8.3713e-01
epoch 62 loss = 8.3711e-01
epoch 63 loss = 8.371e-01
epoch 64 loss = 8.3709e-01
epoch 65 loss = 8.3709e-01
epoch 66 loss = 8.3709e-01
epoch 67 loss = 8.3707e-01
epoch 68 loss = 8.3707e-01
epoch 69 loss = 8.3703e-01
epoch 70 loss = 8.3703e-01
epoch 71 loss = 8.3703e-01
epoch 72 loss = 8.3703e-01
epoch 73 loss = 8.3702e-01
epoch 74 loss = 8.3701e-01
epoch 75 loss = 8.3701e-01
epoch 76 loss = 8.3698e-01
epoch 77 loss = 8.3698e-01
epoch 78 loss = 8.3698e-01
epoch 79 loss = 8.3697e-01
epoch 80 loss = 8.3697e-01
epoch 81 loss = 8.3696e-01
epoch 82 loss = 8.3695e-01
epoch 83 loss = 8.3695e-01
epoch 84 loss = 8.3694e-01
epoch 85 loss = 8.3693e-01
epoch 86 loss = 8.3693e-01
epoch 87 loss = 8.3693e-01
epoch 88 loss = 8.3692e-01
epoch 89 loss = 8.3692e-01
epoch 90 loss = 8.3691e-01
epoch 91 loss = 8.369e-01
epoch 92 loss = 8.3687e-01
epoch 93 loss = 8.3686e-01
epoch 94 loss = 8.3686e-01
epoch 95 loss = 8.3685e-01
epoch 96 loss = 8.3684e-01
epoch 97 loss = 8.3683e-01
epoch 98 loss = 8.3683e-01
epoch 99 loss = 8.3682e-01
epoch 100 loss = 8.3682e-01
epoch 101 loss = 8.3682e-01
epoch 102 loss = 8.3682e-01
epoch 103 loss = 8.3681e-01
epoch 104 loss = 8.3681e-01
epoch 105 loss = 8.368e-01
epoch 106 loss = 8.3679e-01
epoch 107 loss = 8.3679e-01
epoch 108 loss = 8.3679e-01
epoch 109 loss = 8.3678e-01
epoch 110 loss = 8.3678e-01
epoch 111 loss = 8.3677e-01
epoch 112 loss = 8.3676e-01
epoch 113 loss = 8.3676e-01
epoch 114 loss = 8.3676e-01
epoch 115 loss = 8.3676e-01
epoch 116 loss = 8.3676e-01
epoch 117 loss = 8.3676e-01
*************** END FIRST PHASE ***************
* Training time: 5.66278600692749s
* Average epoch time: 0.04839988040108966s
************ MESH READING COMPLETE ************
* Dimension of the problem: 2D
* Elements type: t3: 3-node triangle
* Number of Dofs: 3608
* Refinement level: 3
**************** START TRAINING ***************
epoch 2 loss = 8.3259e-01
epoch 3 loss = 8.3243e-01
epoch 4 loss = 8.3232e-01
epoch 5 loss = 8.3225e-01
epoch 6 loss = 8.3221e-01
epoch 7 loss = 8.3219e-01
epoch 8 loss = 8.3213e-01
epoch 9 loss = 8.3208e-01
epoch 10 loss = 8.3205e-01
epoch 11 loss = 8.3199e-01
epoch 12 loss = 8.3197e-01
epoch 13 loss = 8.3194e-01
epoch 14 loss = 8.3191e-01
epoch 15 loss = 8.3189e-01
epoch 16 loss = 8.3187e-01
epoch 17 loss = 8.3185e-01
epoch 18 loss = 8.3185e-01
epoch 19 loss = 8.3183e-01
epoch 20 loss = 8.3182e-01
epoch 21 loss = 8.3180e-01
epoch 22 loss = 8.3170e-01
epoch 23 loss = 8.3168e-01
epoch 24 loss = 8.3167e-01
epoch 25 loss = 8.3165e-01
epoch 26 loss = 8.3163e-01
epoch 27 loss = 8.3163e-01
epoch 28 loss = 8.3161e-01
epoch 29 loss = 8.3158e-01
epoch 30 loss = 8.3157e-01
epoch 31 loss = 8.3156e-01
epoch 32 loss = 8.3155e-01
epoch 33 loss = 8.3154e-01
epoch 34 loss = 8.3153e-01
epoch 35 loss = 8.3153e-01
epoch 36 loss = 8.3152e-01
epoch 37 loss = 8.3151e-01
epoch 38 loss = 8.315e-01
epoch 39 loss = 8.3149e-01
epoch 40 loss = 8.3148e-01
epoch 41 loss = 8.3148e-01
epoch 42 loss = 8.3147e-01
epoch 43 loss = 8.3147e-01
epoch 44 loss = 8.3146e-01
epoch 45 loss = 8.3146e-01
epoch 46 loss = 8.3145e-01
epoch 47 loss = 8.3145e-01
epoch 48 loss = 8.3144e-01
epoch 49 loss = 8.3144e-01
epoch 50 loss = 8.3143e-01
epoch 51 loss = 8.3142e-01
epoch 52 loss = 8.3142e-01
epoch 53 loss = 8.3141e-01
epoch 54 loss = 8.3141e-01
epoch 55 loss = 8.3141e-01
epoch 56 loss = 8.3140e-01
epoch 57 loss = 8.314e-01
epoch 58 loss = 8.3139e-01
epoch 59 loss = 8.3139e-01
epoch 60 loss = 8.3139e-01
epoch 61 loss = 8.3138e-01
epoch 62 loss = 8.3138e-01
epoch 63 loss = 8.3138e-01
epoch 64 loss = 8.3137e-01
epoch 65 loss = 8.3137e-01
epoch 66 loss = 8.3137e-01
epoch 67 loss = 8.3136e-01
epoch 68 loss = 8.3135e-01
epoch 69 loss = 8.3135e-01
epoch 70 loss = 8.3134e-01
epoch 71 loss = 8.3134e-01
epoch 72 loss = 8.3134e-01
epoch 73 loss = 8.3133e-01
epoch 74 loss = 8.3133e-01
epoch 75 loss = 8.3133e-01
epoch 76 loss = 8.3132e-01
epoch 77 loss = 8.3132e-01
epoch 78 loss = 8.3132e-01
epoch 79 loss = 8.3131e-01
epoch 80 loss = 8.3131e-01
epoch 81 loss = 8.3131e-01
epoch 82 loss = 8.3131e-01
epoch 83 loss = 8.3130e-01
epoch 84 loss = 8.3130e-01
epoch 85 loss = 8.313e-01
epoch 86 loss = 8.313e-01
epoch 87 loss = 8.313e-01
epoch 88 loss = 8.3129e-01
epoch 89 loss = 8.3129e-01
epoch 90 loss = 8.3129e-01
epoch 91 loss = 8.3128e-01
epoch 92 loss = 8.3127e-01
epoch 93 loss = 8.3127e-01
epoch 94 loss = 8.3127e-01
epoch 95 loss = 8.3127e-01
epoch 96 loss = 8.3126e-01
epoch 97 loss = 8.3126e-01
epoch 98 loss = 8.3126e-01
epoch 99 loss = 8.3126e-01
epoch 100 loss = 8.3125e-01
epoch 101 loss = 8.3125e-01
epoch 102 loss = 8.3125e-01
epoch 103 loss = 8.3125e-01
epoch 104 loss = 8.3124e-01
epoch 105 loss = 8.3124e-01
epoch 106 loss = 8.3124e-01
epoch 107 loss = 8.3124e-01
epoch 108 loss = 8.3124e-01
epoch 109 loss = 8.3123e-01
epoch 110 loss = 8.3123e-01
epoch 111 loss = 8.3123e-01
epoch 112 loss = 8.3123e-01
epoch 113 loss = 8.3123e-01
epoch 114 loss = 8.3122e-01
epoch 115 loss = 8.3122e-01
epoch 116 loss = 8.3122e-01
epoch 117 loss = 8.3122e-01
epoch 118 loss = 8.3122e-01
epoch 119 loss = 8.3121e-01
epoch 120 loss = 8.3121e-01
epoch 121 loss = 8.3121e-01
epoch 122 loss = 8.3121e-01
epoch 123 loss = 8.3120e-01
epoch 124 loss = 8.3120e-01
epoch 125 loss = 8.3120e-01
epoch 126 loss = 8.312e-01
epoch 127 loss = 8.312e-01
epoch 128 loss = 8.3119e-01
epoch 129 loss = 8.3119e-01
epoch 130 loss = 8.3119e-01
epoch 131 loss = 8.3119e-01
epoch 132 loss = 8.3119e-01
epoch 133 loss = 8.3118e-01
epoch 134 loss = 8.3118e-01
epoch 135 loss = 8.3118e-01
epoch 136 loss = 8.3117e-01
epoch 137 loss = 8.3117e-01
epoch 138 loss = 8.3117e-01
epoch 139 loss = 8.3117e-01
epoch 140 loss = 8.3117e-01
epoch 141 loss = 8.3116e-01
epoch 142 loss = 8.3116e-01
epoch 143 loss = 8.3116e-01
epoch 144 loss = 8.3115e-01
epoch 145 loss = 8.3115e-01
epoch 146 loss = 8.3115e-01
epoch 147 loss = 8.3115e-01
epoch 148 loss = 8.3115e-01
epoch 149 loss = 8.3115e-01
epoch 150 loss = 8.3114e-01
epoch 151 loss = 8.3114e-01
epoch 152 loss = 8.3113e-01
epoch 153 loss = 8.3113e-01
epoch 154 loss = 8.3113e-01
epoch 155 loss = 8.3113e-01
epoch 156 loss = 8.3113e-01
epoch 157 loss = 8.3113e-01
epoch 158 loss = 8.3112e-01
epoch 159 loss = 8.3112e-01
epoch 160 loss = 8.3112e-01
epoch 161 loss = 8.3112e-01
epoch 162 loss = 8.3111e-01
epoch 163 loss = 8.3111e-01
epoch 164 loss = 8.3111e-01
epoch 165 loss = 8.3111e-01
epoch 166 loss = 8.3111e-01
epoch 167 loss = 8.3111e-01
epoch 168 loss = 8.3111e-01
epoch 169 loss = 8.3111e-01
epoch 170 loss = 8.3110e-01
epoch 171 loss = 8.3110e-01
epoch 172 loss = 8.3110e-01
epoch 173 loss = 8.3110e-01
epoch 174 loss = 8.3110e-01
epoch 175 loss = 8.311e-01
epoch 176 loss = 8.311e-01
epoch 177 loss = 8.311e-01
epoch 178 loss = 8.3109e-01
epoch 179 loss = 8.3109e-01
epoch 180 loss = 8.3109e-01
epoch 181 loss = 8.3109e-01
epoch 182 loss = 8.3109e-01
epoch 183 loss = 8.3109e-01
epoch 184 loss = 8.3109e-01
epoch 185 loss = 8.3109e-01
epoch 186 loss = 8.3109e-01
epoch 187 loss = 8.3109e-01
epoch 188 loss = 8.3109e-01
epoch 189 loss = 8.3109e-01
epoch 190 loss = 8.3109e-01
epoch 191 loss = 8.3109e-01
epoch 192 loss = 8.3108e-01
epoch 193 loss = 8.3108e-01
epoch 194 loss = 8.3108e-01
epoch 195 loss = 8.3108e-01
epoch 196 loss = 8.3108e-01
epoch 197 loss = 8.3108e-01
epoch 198 loss = 8.3108e-01
epoch 199 loss = 8.3107e-01
epoch 200 loss = 8.3107e-01
epoch 201 loss = 8.3107e-01
epoch 202 loss = 8.3107e-01
epoch 203 loss = 8.3107e-01
epoch 204 loss = 8.3107e-01
epoch 205 loss = 8.3107e-01
epoch 206 loss = 8.3107e-01
epoch 207 loss = 8.3107e-01
epoch 208 loss = 8.3107e-01
epoch 209 loss = 8.3107e-01
epoch 210 loss = 8.3106e-01
epoch 211 loss = 8.3106e-01
epoch 212 loss = 8.3105e-01
epoch 213 loss = 8.3105e-01
epoch 214 loss = 8.3105e-01
epoch 215 loss = 8.3105e-01
epoch 216 loss = 8.3105e-01
epoch 217 loss = 8.3105e-01
epoch 218 loss = 8.3105e-01
epoch 219 loss = 8.3105e-01
epoch 220 loss = 8.3105e-01
epoch 221 loss = 8.3105e-01
epoch 222 loss = 8.3105e-01
epoch 223 loss = 8.3105e-01
epoch 224 loss = 8.3104e-01
epoch 225 loss = 8.3104e-01
epoch 226 loss = 8.3104e-01
epoch 227 loss = 8.3104e-01
epoch 228 loss = 8.3104e-01
epoch 229 loss = 8.3104e-01
epoch 230 loss = 8.3104e-01
epoch 231 loss = 8.3104e-01
epoch 232 loss = 8.3104e-01
epoch 233 loss = 8.3104e-01
epoch 234 loss = 8.3103e-01
epoch 235 loss = 8.3103e-01
epoch 236 loss = 8.3103e-01
epoch 237 loss = 8.3103e-01
epoch 238 loss = 8.3103e-01
epoch 239 loss = 8.3103e-01
epoch 240 loss = 8.3103e-01
epoch 241 loss = 8.3103e-01
epoch 242 loss = 8.3103e-01
epoch 243 loss = 8.3103e-01
epoch 244 loss = 8.3102e-01
epoch 245 loss = 8.3102e-01
epoch 246 loss = 8.3102e-01
epoch 247 loss = 8.3102e-01
epoch 248 loss = 8.3102e-01
epoch 249 loss = 8.3102e-01
epoch 250 loss = 8.3101e-01
epoch 251 loss = 8.3101e-01
epoch 252 loss = 8.3101e-01
epoch 253 loss = 8.3101e-01
epoch 254 loss = 8.3101e-01
epoch 255 loss = 8.3101e-01
epoch 256 loss = 8.3101e-01
epoch 257 loss = 8.3101e-01
epoch 258 loss = 8.3101e-01
epoch 259 loss = 8.3101e-01
epoch 260 loss = 8.3101e-01
epoch 261 loss = 8.3101e-01
epoch 262 loss = 8.3101e-01
epoch 263 loss = 8.3101e-01
epoch 264 loss = 8.3100e-01
epoch 265 loss = 8.3100e-01
epoch 266 loss = 8.3100e-01
epoch 267 loss = 8.3100e-01
epoch 268 loss = 8.3100e-01
epoch 269 loss = 8.3100e-01
epoch 270 loss = 8.3100e-01
epoch 271 loss = 8.3100e-01
epoch 272 loss = 8.3100e-01
epoch 273 loss = 8.3100e-01
epoch 274 loss = 8.31e-01
epoch 275 loss = 8.31e-01
epoch 276 loss = 8.31e-01
epoch 277 loss = 8.31e-01
epoch 278 loss = 8.31e-01
epoch 279 loss = 8.3099e-01
epoch 280 loss = 8.3099e-01
epoch 281 loss = 8.3099e-01
epoch 282 loss = 8.3099e-01
epoch 283 loss = 8.3099e-01
epoch 284 loss = 8.3099e-01
epoch 285 loss = 8.3099e-01
epoch 286 loss = 8.3099e-01
epoch 287 loss = 8.3099e-01
epoch 288 loss = 8.3099e-01
epoch 289 loss = 8.3099e-01
epoch 290 loss = 8.3099e-01
epoch 291 loss = 8.3099e-01
epoch 292 loss = 8.3098e-01
epoch 293 loss = 8.3098e-01
epoch 294 loss = 8.3098e-01
epoch 295 loss = 8.3098e-01
epoch 296 loss = 8.3098e-01
epoch 297 loss = 8.3098e-01
epoch 298 loss = 8.3098e-01
epoch 299 loss = 8.3098e-01
epoch 300 loss = 8.3098e-01
epoch 301 loss = 8.3098e-01
epoch 302 loss = 8.3098e-01
epoch 303 loss = 8.3098e-01
epoch 304 loss = 8.3097e-01
epoch 305 loss = 8.3097e-01
epoch 306 loss = 8.3097e-01
epoch 307 loss = 8.3097e-01
epoch 308 loss = 8.3097e-01
epoch 309 loss = 8.3097e-01
epoch 310 loss = 8.3097e-01
epoch 311 loss = 8.3097e-01
epoch 312 loss = 8.3097e-01
epoch 313 loss = 8.3097e-01
epoch 314 loss = 8.3097e-01
epoch 315 loss = 8.3097e-01
epoch 316 loss = 8.3097e-01
epoch 317 loss = 8.3097e-01
epoch 318 loss = 8.3096e-01
epoch 319 loss = 8.3096e-01
epoch 320 loss = 8.3096e-01
epoch 321 loss = 8.3096e-01
epoch 322 loss = 8.3096e-01
epoch 323 loss = 8.3096e-01
epoch 324 loss = 8.3096e-01
epoch 325 loss = 8.3096e-01
epoch 326 loss = 8.3096e-01
epoch 327 loss = 8.3096e-01
epoch 328 loss = 8.3096e-01
epoch 329 loss = 8.3096e-01
epoch 330 loss = 8.3096e-01
epoch 331 loss = 8.3096e-01
epoch 332 loss = 8.3096e-01
epoch 333 loss = 8.3096e-01
epoch 334 loss = 8.3096e-01
epoch 335 loss = 8.3095e-01
epoch 336 loss = 8.3095e-01
epoch 337 loss = 8.3095e-01
epoch 338 loss = 8.3095e-01
epoch 339 loss = 8.3095e-01
epoch 340 loss = 8.3095e-01
epoch 341 loss = 8.3095e-01
epoch 342 loss = 8.3095e-01
epoch 343 loss = 8.3095e-01
epoch 344 loss = 8.3095e-01
epoch 345 loss = 8.3095e-01
epoch 346 loss = 8.3095e-01
epoch 347 loss = 8.3095e-01
epoch 348 loss = 8.3095e-01
epoch 349 loss = 8.3095e-01
epoch 350 loss = 8.3095e-01
epoch 351 loss = 8.3095e-01
epoch 352 loss = 8.3095e-01
epoch 353 loss = 8.3095e-01
epoch 354 loss = 8.3095e-01
epoch 355 loss = 8.3095e-01
epoch 356 loss = 8.3095e-01
epoch 357 loss = 8.3095e-01
epoch 358 loss = 8.3095e-01
epoch 359 loss = 8.3095e-01
epoch 360 loss = 8.3095e-01
epoch 361 loss = 8.3095e-01
epoch 362 loss = 8.3095e-01
epoch 363 loss = 8.3095e-01
epoch 364 loss = 8.3095e-01
epoch 365 loss = 8.3095e-01
epoch 366 loss = 8.3094e-01
epoch 367 loss = 8.3094e-01
epoch 368 loss = 8.3094e-01
epoch 369 loss = 8.3094e-01
epoch 370 loss = 8.3094e-01
epoch 371 loss = 8.3094e-01
epoch 372 loss = 8.3094e-01
epoch 373 loss = 8.3094e-01
epoch 374 loss = 8.3094e-01
epoch 375 loss = 8.3094e-01
epoch 376 loss = 8.3094e-01
epoch 377 loss = 8.3094e-01
epoch 378 loss = 8.3094e-01
epoch 379 loss = 8.3094e-01
epoch 380 loss = 8.3094e-01
epoch 381 loss = 8.3094e-01
epoch 382 loss = 8.3094e-01
epoch 383 loss = 8.3094e-01
epoch 384 loss = 8.3094e-01
epoch 385 loss = 8.3094e-01
epoch 386 loss = 8.3094e-01
epoch 387 loss = 8.3094e-01
epoch 388 loss = 8.3094e-01
epoch 389 loss = 8.3094e-01
epoch 390 loss = 8.3094e-01
epoch 391 loss = 8.3093e-01
epoch 392 loss = 8.3093e-01
epoch 393 loss = 8.3093e-01
epoch 394 loss = 8.3093e-01
epoch 395 loss = 8.3093e-01
epoch 396 loss = 8.3093e-01
epoch 397 loss = 8.3093e-01
epoch 398 loss = 8.3093e-01
epoch 399 loss = 8.3093e-01
epoch 400 loss = 8.3093e-01
epoch 401 loss = 8.3093e-01
epoch 402 loss = 8.3093e-01
epoch 403 loss = 8.3093e-01
epoch 404 loss = 8.3093e-01
epoch 405 loss = 8.3093e-01
epoch 406 loss = 8.3093e-01
epoch 407 loss = 8.3093e-01
epoch 408 loss = 8.3093e-01
epoch 409 loss = 8.3093e-01
epoch 410 loss = 8.3093e-01
epoch 411 loss = 8.3093e-01
epoch 412 loss = 8.3093e-01
epoch 413 loss = 8.3093e-01
epoch 414 loss = 8.3093e-01
epoch 415 loss = 8.3093e-01
epoch 416 loss = 8.3092e-01
epoch 417 loss = 8.3092e-01
epoch 418 loss = 8.3092e-01
epoch 419 loss = 8.3092e-01
epoch 420 loss = 8.3092e-01
epoch 421 loss = 8.3092e-01
epoch 422 loss = 8.3092e-01
epoch 423 loss = 8.3092e-01
epoch 424 loss = 8.3092e-01
epoch 425 loss = 8.3092e-01
epoch 426 loss = 8.3092e-01
epoch 427 loss = 8.3092e-01
epoch 428 loss = 8.3092e-01
epoch 429 loss = 8.3092e-01
epoch 430 loss = 8.3092e-01
epoch 431 loss = 8.3092e-01
epoch 432 loss = 8.3092e-01
epoch 433 loss = 8.3092e-01
epoch 434 loss = 8.3092e-01
epoch 435 loss = 8.3092e-01
*************** END FIRST PHASE ***************
* Training time: 39.63078808784485s
* Average epoch time: 0.09110525997205712s
***************** END TRAINING ****************
* Training time: 54.86447310447693s
_ _ ____ ___ __ __
| \ | | ___ _ _| _ \ / _ \| \/ |
| \| |/ _ \ | | | |_) | | | | |\/| |
| |\ | __/ |_| | _ <| |_| | | | |
|_| \_|\___|\__,_|_| \_\ ___/|_| |_|
2024.09.18
************ MESH READING COMPLETE ************
* Dimension of the problem: 2D
* Elements type: t3: 3-node triangle
* Number of Dofs: 88
* Refinement level: 0
**************** START TRAINING ***************
epoch 2 loss = 9.4093e-01
epoch 3 loss = 9.3267e-01
epoch 4 loss = 9.3022e-01
epoch 5 loss = 9.3013e-01
epoch 6 loss = 9.2284e-01
epoch 7 loss = 9.2015e-01
epoch 8 loss = 9.1919e-01
epoch 9 loss = 9.1822e-01
epoch 10 loss = 9.1781e-01
epoch 11 loss = 9.1773e-01
epoch 12 loss = 9.1767e-01
epoch 13 loss = 9.1766e-01
epoch 14 loss = 9.1766e-01
epoch 15 loss = 9.1766e-01
epoch 16 loss = 9.1766e-01
epoch 17 loss = 9.1766e-01
epoch 18 loss = 9.1766e-01
*************** END FIRST PHASE ***************
* Training time: 0.656785249710083s
* Average epoch time: 0.036488069428337946s
************ MESH READING COMPLETE ************
* Dimension of the problem: 2D
* Elements type: t3: 3-node triangle
* Number of Dofs: 288
* Refinement level: 1
**************** START TRAINING ***************
epoch 2 loss = 8.6951e-01
epoch 3 loss = 8.6357e-01
epoch 4 loss = 8.6258e-01
epoch 5 loss = 8.6034e-01
epoch 6 loss = 8.5937e-01
epoch 7 loss = 8.5907e-01
epoch 8 loss = 8.5867e-01
epoch 9 loss = 8.5831e-01
epoch 10 loss = 8.5799e-01
epoch 11 loss = 8.5773e-01
epoch 12 loss = 8.5751e-01
epoch 13 loss = 8.5721e-01
epoch 14 loss = 8.5682e-01
epoch 15 loss = 8.5671e-01
epoch 16 loss = 8.5660e-01
epoch 17 loss = 8.5649e-01
epoch 18 loss = 8.5642e-01
epoch 19 loss = 8.5617e-01
epoch 20 loss = 8.5574e-01
epoch 21 loss = 8.5540e-01
epoch 22 loss = 8.5509e-01
epoch 23 loss = 8.5496e-01
epoch 24 loss = 8.5481e-01
epoch 25 loss = 8.5471e-01
epoch 26 loss = 8.5462e-01
epoch 27 loss = 8.5453e-01
epoch 28 loss = 8.5449e-01
epoch 29 loss = 8.5440e-01
epoch 30 loss = 8.5432e-01
epoch 31 loss = 8.5425e-01
epoch 32 loss = 8.5420e-01
epoch 33 loss = 8.5417e-01
epoch 34 loss = 8.5415e-01
epoch 35 loss = 8.5413e-01
epoch 36 loss = 8.5408e-01
epoch 37 loss = 8.5405e-01
epoch 38 loss = 8.5399e-01
epoch 39 loss = 8.5393e-01
epoch 40 loss = 8.5389e-01
epoch 41 loss = 8.5387e-01
epoch 42 loss = 8.5385e-01
epoch 43 loss = 8.5382e-01
epoch 44 loss = 8.5380e-01
epoch 45 loss = 8.5378e-01
epoch 46 loss = 8.5375e-01
epoch 47 loss = 8.5372e-01
epoch 48 loss = 8.5369e-01
epoch 49 loss = 8.5367e-01
epoch 50 loss = 8.5364e-01
epoch 51 loss = 8.5358e-01
epoch 52 loss = 8.5351e-01
epoch 53 loss = 8.5347e-01
epoch 54 loss = 8.5344e-01
epoch 55 loss = 8.5338e-01
epoch 56 loss = 8.5337e-01
epoch 57 loss = 8.5316e-01
epoch 58 loss = 8.5309e-01
epoch 59 loss = 8.5297e-01
epoch 60 loss = 8.5295e-01
epoch 61 loss = 8.5290e-01
epoch 62 loss = 8.5286e-01
epoch 63 loss = 8.5283e-01
epoch 64 loss = 8.5281e-01
epoch 65 loss = 8.5277e-01
epoch 66 loss = 8.5276e-01
epoch 67 loss = 8.5274e-01
epoch 68 loss = 8.5263e-01
epoch 69 loss = 8.5259e-01
epoch 70 loss = 8.5254e-01
epoch 71 loss = 8.5251e-01
epoch 72 loss = 8.5249e-01
epoch 73 loss = 8.5247e-01
epoch 74 loss = 8.5246e-01
epoch 75 loss = 8.5244e-01
epoch 76 loss = 8.5242e-01
epoch 77 loss = 8.5241e-01
epoch 78 loss = 8.5240e-01
epoch 79 loss = 8.5239e-01
epoch 80 loss = 8.5238e-01
epoch 81 loss = 8.5238e-01
epoch 82 loss = 8.5237e-01
epoch 83 loss = 8.5236e-01
epoch 84 loss = 8.5234e-01
epoch 85 loss = 8.5234e-01
epoch 86 loss = 8.5232e-01
epoch 87 loss = 8.5230e-01
epoch 88 loss = 8.5229e-01
epoch 89 loss = 8.5228e-01
epoch 90 loss = 8.5227e-01
epoch 91 loss = 8.5227e-01
epoch 92 loss = 8.5225e-01
epoch 93 loss = 8.5224e-01
epoch 94 loss = 8.5224e-01
epoch 95 loss = 8.5223e-01
epoch 96 loss = 8.5222e-01
epoch 97 loss = 8.5221e-01
epoch 98 loss = 8.5220e-01
epoch 99 loss = 8.5219e-01
epoch 100 loss = 8.5216e-01
epoch 101 loss = 8.5213e-01
epoch 102 loss = 8.5210e-01
epoch 103 loss = 8.5209e-01
epoch 104 loss = 8.5208e-01
epoch 105 loss = 8.5208e-01
epoch 106 loss = 8.5207e-01
epoch 107 loss = 8.5207e-01
epoch 108 loss = 8.5206e-01
epoch 109 loss = 8.5205e-01
epoch 110 loss = 8.5205e-01
epoch 111 loss = 8.5204e-01
epoch 112 loss = 8.5203e-01
epoch 113 loss = 8.5203e-01
epoch 114 loss = 8.5202e-01
epoch 115 loss = 8.5201e-01
epoch 116 loss = 8.5200e-01
epoch 117 loss = 8.5199e-01
epoch 118 loss = 8.5199e-01
epoch 119 loss = 8.5198e-01
epoch 120 loss = 8.5198e-01
epoch 121 loss = 8.5197e-01
epoch 122 loss = 8.5197e-01
epoch 123 loss = 8.5197e-01
epoch 124 loss = 8.5196e-01
epoch 125 loss = 8.5196e-01
epoch 126 loss = 8.5195e-01
epoch 127 loss = 8.5195e-01
epoch 128 loss = 8.5194e-01
epoch 129 loss = 8.5193e-01
epoch 130 loss = 8.5193e-01
epoch 131 loss = 8.5193e-01
epoch 132 loss = 8.5192e-01
epoch 133 loss = 8.5192e-01
epoch 134 loss = 8.5192e-01
epoch 135 loss = 8.5192e-01
epoch 136 loss = 8.5191e-01
epoch 137 loss = 8.5191e-01
epoch 138 loss = 8.519e-01
epoch 139 loss = 8.519e-01
epoch 140 loss = 8.5189e-01
epoch 141 loss = 8.5189e-01
epoch 142 loss = 8.5189e-01
epoch 143 loss = 8.5188e-01
epoch 144 loss = 8.5188e-01
epoch 145 loss = 8.5188e-01
epoch 146 loss = 8.5188e-01
epoch 147 loss = 8.5187e-01
epoch 148 loss = 8.5186e-01
epoch 149 loss = 8.5186e-01
epoch 150 loss = 8.5186e-01
epoch 151 loss = 8.5185e-01
epoch 152 loss = 8.5184e-01
epoch 153 loss = 8.5183e-01
epoch 154 loss = 8.5182e-01
epoch 155 loss = 8.5182e-01
epoch 156 loss = 8.5182e-01
epoch 157 loss = 8.5181e-01
epoch 158 loss = 8.5181e-01
epoch 159 loss = 8.5181e-01
epoch 160 loss = 8.5181e-01
epoch 161 loss = 8.5181e-01
epoch 162 loss = 8.5180e-01
epoch 163 loss = 8.518e-01
epoch 164 loss = 8.518e-01
epoch 165 loss = 8.518e-01
epoch 166 loss = 8.518e-01
epoch 167 loss = 8.518e-01
epoch 168 loss = 8.5179e-01
epoch 169 loss = 8.5179e-01
epoch 170 loss = 8.5179e-01
epoch 171 loss = 8.5179e-01
epoch 172 loss = 8.5179e-01
epoch 173 loss = 8.5179e-01
epoch 174 loss = 8.5179e-01
epoch 175 loss = 8.5179e-01
epoch 176 loss = 8.5179e-01
epoch 177 loss = 8.5179e-01
epoch 178 loss = 8.5178e-01
epoch 179 loss = 8.5178e-01
epoch 180 loss = 8.5178e-01
epoch 181 loss = 8.5178e-01
epoch 182 loss = 8.5178e-01
epoch 183 loss = 8.5175e-01
epoch 184 loss = 8.5175e-01
epoch 185 loss = 8.5175e-01
epoch 186 loss = 8.5175e-01
epoch 187 loss = 8.5175e-01
epoch 188 loss = 8.5174e-01
epoch 189 loss = 8.5174e-01
epoch 190 loss = 8.5174e-01
epoch 191 loss = 8.5174e-01
epoch 192 loss = 8.5174e-01
epoch 193 loss = 8.5174e-01
epoch 194 loss = 8.5174e-01
epoch 195 loss = 8.5174e-01
epoch 196 loss = 8.5174e-01
epoch 197 loss = 8.5174e-01
epoch 198 loss = 8.5173e-01
epoch 199 loss = 8.5173e-01
epoch 200 loss = 8.5173e-01
epoch 201 loss = 8.5173e-01
epoch 202 loss = 8.5173e-01
epoch 203 loss = 8.5173e-01
*************** END FIRST PHASE ***************
* Training time: 8.90627408027649s
* Average epoch time: 0.04387327133141128s
************ MESH READING COMPLETE ************
* Dimension of the problem: 2D
* Elements type: t3: 3-node triangle
* Number of Dofs: 968
* Refinement level: 2
**************** START TRAINING ***************
epoch 2 loss = 8.416e-01
epoch 3 loss = 8.4073e-01
epoch 4 loss = 8.4028e-01
epoch 5 loss = 8.4001e-01
epoch 6 loss = 8.3982e-01
epoch 7 loss = 8.3939e-01
epoch 8 loss = 8.3927e-01
epoch 9 loss = 8.3907e-01
epoch 10 loss = 8.3898e-01
epoch 11 loss = 8.3876e-01
epoch 12 loss = 8.3867e-01
epoch 13 loss = 8.3855e-01
epoch 14 loss = 8.3846e-01
epoch 15 loss = 8.3843e-01
epoch 16 loss = 8.3834e-01
epoch 17 loss = 8.3829e-01
epoch 18 loss = 8.3822e-01
epoch 19 loss = 8.3815e-01
epoch 20 loss = 8.3809e-01
epoch 21 loss = 8.3802e-01
epoch 22 loss = 8.3798e-01
epoch 23 loss = 8.3793e-01
epoch 24 loss = 8.3789e-01
epoch 25 loss = 8.3788e-01
epoch 26 loss = 8.3784e-01
epoch 27 loss = 8.3781e-01
epoch 28 loss = 8.3777e-01
epoch 29 loss = 8.3774e-01
epoch 30 loss = 8.3771e-01
epoch 31 loss = 8.3768e-01
epoch 32 loss = 8.3763e-01
epoch 33 loss = 8.3759e-01
epoch 34 loss = 8.3756e-01
epoch 35 loss = 8.3754e-01
epoch 36 loss = 8.3752e-01
epoch 37 loss = 8.3750e-01
epoch 38 loss = 8.3748e-01
epoch 39 loss = 8.3746e-01
epoch 40 loss = 8.3746e-01
epoch 41 loss = 8.3744e-01
epoch 42 loss = 8.3743e-01
epoch 43 loss = 8.3742e-01
epoch 44 loss = 8.3741e-01
epoch 45 loss = 8.3739e-01
epoch 46 loss = 8.3737e-01
epoch 47 loss = 8.3736e-01
epoch 48 loss = 8.3734e-01
epoch 49 loss = 8.3733e-01
epoch 50 loss = 8.3732e-01
epoch 51 loss = 8.3731e-01
epoch 52 loss = 8.373e-01
epoch 53 loss = 8.3728e-01
epoch 54 loss = 8.3726e-01
epoch 55 loss = 8.3726e-01
epoch 56 loss = 8.3724e-01
epoch 57 loss = 8.3723e-01
epoch 58 loss = 8.3717e-01
epoch 59 loss = 8.3716e-01
epoch 60 loss = 8.3715e-01
epoch 61 loss = 8.3713e-01
epoch 62 loss = 8.3711e-01
epoch 63 loss = 8.371e-01
epoch 64 loss = 8.3709e-01
epoch 65 loss = 8.3709e-01
epoch 66 loss = 8.3709e-01
epoch 67 loss = 8.3707e-01
epoch 68 loss = 8.3707e-01
epoch 69 loss = 8.3703e-01
epoch 70 loss = 8.3703e-01
epoch 71 loss = 8.3703e-01
epoch 72 loss = 8.3703e-01
epoch 73 loss = 8.3702e-01
epoch 74 loss = 8.3701e-01
epoch 75 loss = 8.3701e-01
epoch 76 loss = 8.3698e-01
epoch 77 loss = 8.3698e-01
epoch 78 loss = 8.3698e-01
epoch 79 loss = 8.3697e-01
epoch 80 loss = 8.3697e-01
epoch 81 loss = 8.3696e-01
epoch 82 loss = 8.3695e-01
epoch 83 loss = 8.3695e-01
epoch 84 loss = 8.3694e-01
epoch 85 loss = 8.3693e-01
epoch 86 loss = 8.3693e-01
epoch 87 loss = 8.3693e-01
epoch 88 loss = 8.3692e-01
epoch 89 loss = 8.3692e-01
epoch 90 loss = 8.3691e-01
epoch 91 loss = 8.369e-01
epoch 92 loss = 8.3687e-01
epoch 93 loss = 8.3686e-01
epoch 94 loss = 8.3686e-01
epoch 95 loss = 8.3685e-01
epoch 96 loss = 8.3684e-01
epoch 97 loss = 8.3683e-01
epoch 98 loss = 8.3683e-01
epoch 99 loss = 8.3682e-01
epoch 100 loss = 8.3682e-01
epoch 101 loss = 8.3682e-01
epoch 102 loss = 8.3682e-01
epoch 103 loss = 8.3681e-01
epoch 104 loss = 8.3681e-01
epoch 105 loss = 8.368e-01
epoch 106 loss = 8.3679e-01
epoch 107 loss = 8.3679e-01
epoch 108 loss = 8.3679e-01
epoch 109 loss = 8.3678e-01
epoch 110 loss = 8.3678e-01
epoch 111 loss = 8.3677e-01
epoch 112 loss = 8.3676e-01
epoch 113 loss = 8.3676e-01
epoch 114 loss = 8.3676e-01
epoch 115 loss = 8.3676e-01
epoch 116 loss = 8.3676e-01
epoch 117 loss = 8.3676e-01
*************** END FIRST PHASE ***************
* Training time: 5.668468952178955s
* Average epoch time: 0.04844845258272611s
************ MESH READING COMPLETE ************
* Dimension of the problem: 2D
* Elements type: t3: 3-node triangle
* Number of Dofs: 3608
* Refinement level: 3
**************** START TRAINING ***************
epoch 2 loss = 8.3259e-01
epoch 3 loss = 8.3243e-01
epoch 4 loss = 8.3232e-01
epoch 5 loss = 8.3225e-01
epoch 6 loss = 8.3221e-01
epoch 7 loss = 8.3219e-01
epoch 8 loss = 8.3213e-01
epoch 9 loss = 8.3208e-01
epoch 10 loss = 8.3205e-01
epoch 11 loss = 8.3199e-01
epoch 12 loss = 8.3197e-01
epoch 13 loss = 8.3194e-01
epoch 14 loss = 8.3191e-01
epoch 15 loss = 8.3189e-01
epoch 16 loss = 8.3187e-01
epoch 17 loss = 8.3185e-01
epoch 18 loss = 8.3185e-01
epoch 19 loss = 8.3183e-01
epoch 20 loss = 8.3182e-01
epoch 21 loss = 8.3180e-01
epoch 22 loss = 8.3170e-01
epoch 23 loss = 8.3168e-01
epoch 24 loss = 8.3167e-01
epoch 25 loss = 8.3165e-01
epoch 26 loss = 8.3163e-01
epoch 27 loss = 8.3163e-01
epoch 28 loss = 8.3161e-01
epoch 29 loss = 8.3158e-01
epoch 30 loss = 8.3157e-01
epoch 31 loss = 8.3156e-01
epoch 32 loss = 8.3155e-01
epoch 33 loss = 8.3154e-01
epoch 34 loss = 8.3153e-01
epoch 35 loss = 8.3153e-01
epoch 36 loss = 8.3152e-01
epoch 37 loss = 8.3151e-01
epoch 38 loss = 8.315e-01
epoch 39 loss = 8.3149e-01
epoch 40 loss = 8.3148e-01
epoch 41 loss = 8.3148e-01
epoch 42 loss = 8.3147e-01
epoch 43 loss = 8.3147e-01
epoch 44 loss = 8.3146e-01
epoch 45 loss = 8.3146e-01
epoch 46 loss = 8.3145e-01
epoch 47 loss = 8.3145e-01
epoch 48 loss = 8.3144e-01
epoch 49 loss = 8.3144e-01
epoch 50 loss = 8.3143e-01
epoch 51 loss = 8.3142e-01
epoch 52 loss = 8.3142e-01
epoch 53 loss = 8.3141e-01
epoch 54 loss = 8.3141e-01
epoch 55 loss = 8.3141e-01
epoch 56 loss = 8.3140e-01
epoch 57 loss = 8.314e-01
epoch 58 loss = 8.3139e-01
epoch 59 loss = 8.3139e-01
epoch 60 loss = 8.3139e-01
epoch 61 loss = 8.3138e-01
epoch 62 loss = 8.3138e-01
epoch 63 loss = 8.3138e-01
epoch 64 loss = 8.3137e-01
epoch 65 loss = 8.3137e-01
epoch 66 loss = 8.3137e-01
epoch 67 loss = 8.3136e-01
epoch 68 loss = 8.3135e-01
epoch 69 loss = 8.3135e-01
epoch 70 loss = 8.3134e-01
epoch 71 loss = 8.3134e-01
epoch 72 loss = 8.3134e-01
epoch 73 loss = 8.3133e-01
epoch 74 loss = 8.3133e-01
epoch 75 loss = 8.3133e-01
epoch 76 loss = 8.3132e-01
epoch 77 loss = 8.3132e-01
epoch 78 loss = 8.3132e-01
epoch 79 loss = 8.3131e-01
epoch 80 loss = 8.3131e-01
epoch 81 loss = 8.3131e-01
epoch 82 loss = 8.3131e-01
epoch 83 loss = 8.3130e-01
epoch 84 loss = 8.3130e-01
epoch 85 loss = 8.313e-01
epoch 86 loss = 8.313e-01
epoch 87 loss = 8.313e-01
epoch 88 loss = 8.3129e-01
epoch 89 loss = 8.3129e-01
epoch 90 loss = 8.3129e-01
epoch 91 loss = 8.3128e-01
epoch 92 loss = 8.3127e-01
epoch 93 loss = 8.3127e-01
epoch 94 loss = 8.3127e-01
epoch 95 loss = 8.3127e-01
epoch 96 loss = 8.3126e-01
epoch 97 loss = 8.3126e-01
epoch 98 loss = 8.3126e-01
epoch 99 loss = 8.3126e-01
epoch 100 loss = 8.3125e-01
epoch 101 loss = 8.3125e-01
epoch 102 loss = 8.3125e-01
epoch 103 loss = 8.3125e-01
epoch 104 loss = 8.3124e-01
epoch 105 loss = 8.3124e-01
epoch 106 loss = 8.3124e-01
epoch 107 loss = 8.3124e-01
epoch 108 loss = 8.3124e-01
epoch 109 loss = 8.3123e-01
epoch 110 loss = 8.3123e-01
epoch 111 loss = 8.3123e-01
epoch 112 loss = 8.3123e-01
epoch 113 loss = 8.3123e-01
epoch 114 loss = 8.3122e-01
epoch 115 loss = 8.3122e-01
epoch 116 loss = 8.3122e-01
epoch 117 loss = 8.3122e-01
epoch 118 loss = 8.3122e-01
epoch 119 loss = 8.3121e-01
epoch 120 loss = 8.3121e-01
epoch 121 loss = 8.3121e-01
epoch 122 loss = 8.3121e-01
epoch 123 loss = 8.3120e-01
epoch 124 loss = 8.3120e-01
epoch 125 loss = 8.3120e-01
epoch 126 loss = 8.312e-01
epoch 127 loss = 8.312e-01
epoch 128 loss = 8.3119e-01
epoch 129 loss = 8.3119e-01
epoch 130 loss = 8.3119e-01
epoch 131 loss = 8.3119e-01
epoch 132 loss = 8.3119e-01
epoch 133 loss = 8.3118e-01
epoch 134 loss = 8.3118e-01
epoch 135 loss = 8.3118e-01
epoch 136 loss = 8.3117e-01
epoch 137 loss = 8.3117e-01
epoch 138 loss = 8.3117e-01
epoch 139 loss = 8.3117e-01
epoch 140 loss = 8.3117e-01
epoch 141 loss = 8.3116e-01
epoch 142 loss = 8.3116e-01
epoch 143 loss = 8.3116e-01
epoch 144 loss = 8.3115e-01
epoch 145 loss = 8.3115e-01
epoch 146 loss = 8.3115e-01
epoch 147 loss = 8.3115e-01
epoch 148 loss = 8.3115e-01
epoch 149 loss = 8.3115e-01
epoch 150 loss = 8.3114e-01
epoch 151 loss = 8.3114e-01
epoch 152 loss = 8.3113e-01
epoch 153 loss = 8.3113e-01
epoch 154 loss = 8.3113e-01
epoch 155 loss = 8.3113e-01
epoch 156 loss = 8.3113e-01
epoch 157 loss = 8.3113e-01
epoch 158 loss = 8.3112e-01
epoch 159 loss = 8.3112e-01
epoch 160 loss = 8.3112e-01
epoch 161 loss = 8.3112e-01
epoch 162 loss = 8.3111e-01
epoch 163 loss = 8.3111e-01
epoch 164 loss = 8.3111e-01
epoch 165 loss = 8.3111e-01
epoch 166 loss = 8.3111e-01
epoch 167 loss = 8.3111e-01
epoch 168 loss = 8.3111e-01
epoch 169 loss = 8.3111e-01
epoch 170 loss = 8.3110e-01
epoch 171 loss = 8.3110e-01
epoch 172 loss = 8.3110e-01
epoch 173 loss = 8.3110e-01
epoch 174 loss = 8.3110e-01
epoch 175 loss = 8.311e-01
epoch 176 loss = 8.311e-01
epoch 177 loss = 8.311e-01
epoch 178 loss = 8.3109e-01
epoch 179 loss = 8.3109e-01
epoch 180 loss = 8.3109e-01
epoch 181 loss = 8.3109e-01
epoch 182 loss = 8.3109e-01
epoch 183 loss = 8.3109e-01
epoch 184 loss = 8.3109e-01
epoch 185 loss = 8.3109e-01
epoch 186 loss = 8.3109e-01
epoch 187 loss = 8.3109e-01
epoch 188 loss = 8.3109e-01
epoch 189 loss = 8.3109e-01
epoch 190 loss = 8.3109e-01
epoch 191 loss = 8.3109e-01
epoch 192 loss = 8.3108e-01
epoch 193 loss = 8.3108e-01
epoch 194 loss = 8.3108e-01
epoch 195 loss = 8.3108e-01
epoch 196 loss = 8.3108e-01
epoch 197 loss = 8.3108e-01
epoch 198 loss = 8.3108e-01
epoch 199 loss = 8.3107e-01
epoch 200 loss = 8.3107e-01
epoch 201 loss = 8.3107e-01
epoch 202 loss = 8.3107e-01
epoch 203 loss = 8.3107e-01
epoch 204 loss = 8.3107e-01
epoch 205 loss = 8.3107e-01
epoch 206 loss = 8.3107e-01
epoch 207 loss = 8.3107e-01
epoch 208 loss = 8.3107e-01
epoch 209 loss = 8.3107e-01
epoch 210 loss = 8.3106e-01
epoch 211 loss = 8.3106e-01
epoch 212 loss = 8.3105e-01
epoch 213 loss = 8.3105e-01
epoch 214 loss = 8.3105e-01
epoch 215 loss = 8.3105e-01
epoch 216 loss = 8.3105e-01
epoch 217 loss = 8.3105e-01
epoch 218 loss = 8.3105e-01
epoch 219 loss = 8.3105e-01
epoch 220 loss = 8.3105e-01
epoch 221 loss = 8.3105e-01
epoch 222 loss = 8.3105e-01
epoch 223 loss = 8.3105e-01
epoch 224 loss = 8.3104e-01
epoch 225 loss = 8.3104e-01
epoch 226 loss = 8.3104e-01
epoch 227 loss = 8.3104e-01
epoch 228 loss = 8.3104e-01
epoch 229 loss = 8.3104e-01
epoch 230 loss = 8.3104e-01
epoch 231 loss = 8.3104e-01
epoch 232 loss = 8.3104e-01
epoch 233 loss = 8.3104e-01
epoch 234 loss = 8.3103e-01
epoch 235 loss = 8.3103e-01
epoch 236 loss = 8.3103e-01
epoch 237 loss = 8.3103e-01
epoch 238 loss = 8.3103e-01
epoch 239 loss = 8.3103e-01
epoch 240 loss = 8.3103e-01
epoch 241 loss = 8.3103e-01
epoch 242 loss = 8.3103e-01
epoch 243 loss = 8.3103e-01
epoch 244 loss = 8.3102e-01
epoch 245 loss = 8.3102e-01
epoch 246 loss = 8.3102e-01
epoch 247 loss = 8.3102e-01
epoch 248 loss = 8.3102e-01
epoch 249 loss = 8.3102e-01
epoch 250 loss = 8.3101e-01
epoch 251 loss = 8.3101e-01
epoch 252 loss = 8.3101e-01
epoch 253 loss = 8.3101e-01
epoch 254 loss = 8.3101e-01
epoch 255 loss = 8.3101e-01
epoch 256 loss = 8.3101e-01
epoch 257 loss = 8.3101e-01
epoch 258 loss = 8.3101e-01
epoch 259 loss = 8.3101e-01
epoch 260 loss = 8.3101e-01
epoch 261 loss = 8.3101e-01
epoch 262 loss = 8.3101e-01
epoch 263 loss = 8.3101e-01
epoch 264 loss = 8.3100e-01
epoch 265 loss = 8.3100e-01
epoch 266 loss = 8.3100e-01
epoch 267 loss = 8.3100e-01
epoch 268 loss = 8.3100e-01
epoch 269 loss = 8.3100e-01
epoch 270 loss = 8.3100e-01
epoch 271 loss = 8.3100e-01
epoch 272 loss = 8.3100e-01
epoch 273 loss = 8.3100e-01
epoch 274 loss = 8.31e-01
epoch 275 loss = 8.31e-01
epoch 276 loss = 8.31e-01
epoch 277 loss = 8.31e-01
epoch 278 loss = 8.31e-01
epoch 279 loss = 8.3099e-01
epoch 280 loss = 8.3099e-01
epoch 281 loss = 8.3099e-01
epoch 282 loss = 8.3099e-01
epoch 283 loss = 8.3099e-01
epoch 284 loss = 8.3099e-01
epoch 285 loss = 8.3099e-01
epoch 286 loss = 8.3099e-01
epoch 287 loss = 8.3099e-01
epoch 288 loss = 8.3099e-01
epoch 289 loss = 8.3099e-01
epoch 290 loss = 8.3099e-01
epoch 291 loss = 8.3099e-01
epoch 292 loss = 8.3098e-01
epoch 293 loss = 8.3098e-01
epoch 294 loss = 8.3098e-01
epoch 295 loss = 8.3098e-01
epoch 296 loss = 8.3098e-01
epoch 297 loss = 8.3098e-01
epoch 298 loss = 8.3098e-01
epoch 299 loss = 8.3098e-01
epoch 300 loss = 8.3098e-01
epoch 301 loss = 8.3098e-01
epoch 302 loss = 8.3098e-01
epoch 303 loss = 8.3098e-01
epoch 304 loss = 8.3097e-01
epoch 305 loss = 8.3097e-01
epoch 306 loss = 8.3097e-01
epoch 307 loss = 8.3097e-01
epoch 308 loss = 8.3097e-01
epoch 309 loss = 8.3097e-01
epoch 310 loss = 8.3097e-01
epoch 311 loss = 8.3097e-01
epoch 312 loss = 8.3097e-01
epoch 313 loss = 8.3097e-01
epoch 314 loss = 8.3097e-01
epoch 315 loss = 8.3097e-01
epoch 316 loss = 8.3097e-01
epoch 317 loss = 8.3097e-01
epoch 318 loss = 8.3096e-01
epoch 319 loss = 8.3096e-01
epoch 320 loss = 8.3096e-01
epoch 321 loss = 8.3096e-01
epoch 322 loss = 8.3096e-01
epoch 323 loss = 8.3096e-01
epoch 324 loss = 8.3096e-01
epoch 325 loss = 8.3096e-01
epoch 326 loss = 8.3096e-01
epoch 327 loss = 8.3096e-01
epoch 328 loss = 8.3096e-01
epoch 329 loss = 8.3096e-01
epoch 330 loss = 8.3096e-01
epoch 331 loss = 8.3096e-01
epoch 332 loss = 8.3096e-01
epoch 333 loss = 8.3096e-01
epoch 334 loss = 8.3096e-01
epoch 335 loss = 8.3095e-01
epoch 336 loss = 8.3095e-01
epoch 337 loss = 8.3095e-01
epoch 338 loss = 8.3095e-01
epoch 339 loss = 8.3095e-01
epoch 340 loss = 8.3095e-01
epoch 341 loss = 8.3095e-01
epoch 342 loss = 8.3095e-01
epoch 343 loss = 8.3095e-01
epoch 344 loss = 8.3095e-01
epoch 345 loss = 8.3095e-01
epoch 346 loss = 8.3095e-01
epoch 347 loss = 8.3095e-01
epoch 348 loss = 8.3095e-01
epoch 349 loss = 8.3095e-01
epoch 350 loss = 8.3095e-01
epoch 351 loss = 8.3095e-01
epoch 352 loss = 8.3095e-01
epoch 353 loss = 8.3095e-01
epoch 354 loss = 8.3095e-01
epoch 355 loss = 8.3095e-01
epoch 356 loss = 8.3095e-01
epoch 357 loss = 8.3095e-01
epoch 358 loss = 8.3095e-01
epoch 359 loss = 8.3095e-01
epoch 360 loss = 8.3095e-01
epoch 361 loss = 8.3095e-01
epoch 362 loss = 8.3095e-01
epoch 363 loss = 8.3095e-01
epoch 364 loss = 8.3095e-01
epoch 365 loss = 8.3095e-01
epoch 366 loss = 8.3094e-01
epoch 367 loss = 8.3094e-01
epoch 368 loss = 8.3094e-01
epoch 369 loss = 8.3094e-01
epoch 370 loss = 8.3094e-01
epoch 371 loss = 8.3094e-01
epoch 372 loss = 8.3094e-01
epoch 373 loss = 8.3094e-01
epoch 374 loss = 8.3094e-01
epoch 375 loss = 8.3094e-01
epoch 376 loss = 8.3094e-01
epoch 377 loss = 8.3094e-01
epoch 378 loss = 8.3094e-01
epoch 379 loss = 8.3094e-01
epoch 380 loss = 8.3094e-01
epoch 381 loss = 8.3094e-01
epoch 382 loss = 8.3094e-01
epoch 383 loss = 8.3094e-01
epoch 384 loss = 8.3094e-01
epoch 385 loss = 8.3094e-01
epoch 386 loss = 8.3094e-01
epoch 387 loss = 8.3094e-01
epoch 388 loss = 8.3094e-01
epoch 389 loss = 8.3094e-01
epoch 390 loss = 8.3094e-01
epoch 391 loss = 8.3093e-01
epoch 392 loss = 8.3093e-01
epoch 393 loss = 8.3093e-01
epoch 394 loss = 8.3093e-01
epoch 395 loss = 8.3093e-01
epoch 396 loss = 8.3093e-01
epoch 397 loss = 8.3093e-01
epoch 398 loss = 8.3093e-01
epoch 399 loss = 8.3093e-01
epoch 400 loss = 8.3093e-01
epoch 401 loss = 8.3093e-01
epoch 402 loss = 8.3093e-01
epoch 403 loss = 8.3093e-01
epoch 404 loss = 8.3093e-01
epoch 405 loss = 8.3093e-01
epoch 406 loss = 8.3093e-01
epoch 407 loss = 8.3093e-01
epoch 408 loss = 8.3093e-01
epoch 409 loss = 8.3093e-01
epoch 410 loss = 8.3093e-01
epoch 411 loss = 8.3093e-01
epoch 412 loss = 8.3093e-01
epoch 413 loss = 8.3093e-01
epoch 414 loss = 8.3093e-01
epoch 415 loss = 8.3093e-01
epoch 416 loss = 8.3092e-01
epoch 417 loss = 8.3092e-01
epoch 418 loss = 8.3092e-01
epoch 419 loss = 8.3092e-01
epoch 420 loss = 8.3092e-01
epoch 421 loss = 8.3092e-01
epoch 422 loss = 8.3092e-01
epoch 423 loss = 8.3092e-01
epoch 424 loss = 8.3092e-01
epoch 425 loss = 8.3092e-01
epoch 426 loss = 8.3092e-01
epoch 427 loss = 8.3092e-01
epoch 428 loss = 8.3092e-01
epoch 429 loss = 8.3092e-01
epoch 430 loss = 8.3092e-01
epoch 431 loss = 8.3092e-01
epoch 432 loss = 8.3092e-01
epoch 433 loss = 8.3092e-01
epoch 434 loss = 8.3092e-01
epoch 435 loss = 8.3092e-01
*************** END FIRST PHASE ***************
* Training time: 40.385820150375366s
* Average epoch time: 0.09284096586293188s
************ MESH READING COMPLETE ************
* Dimension of the problem: 2D
* Elements type: t3: 3-node triangle
* Number of Dofs: 14176
* Refinement level: 4
**************** START TRAINING ***************
epoch 2 loss = 8.2999e-01
epoch 3 loss = 8.2996e-01
epoch 4 loss = 8.2994e-01
epoch 5 loss = 8.2993e-01
epoch 6 loss = 8.2992e-01
epoch 7 loss = 8.2992e-01
epoch 8 loss = 8.2991e-01
epoch 9 loss = 8.2991e-01
epoch 10 loss = 8.299e-01
epoch 11 loss = 8.2989e-01
epoch 12 loss = 8.2988e-01
epoch 13 loss = 8.2988e-01
epoch 14 loss = 8.2987e-01
epoch 15 loss = 8.2987e-01
epoch 16 loss = 8.2987e-01
epoch 17 loss = 8.2987e-01
epoch 18 loss = 8.2986e-01
epoch 19 loss = 8.2986e-01
epoch 20 loss = 8.2986e-01
epoch 21 loss = 8.2986e-01
epoch 22 loss = 8.2985e-01
epoch 23 loss = 8.2985e-01
epoch 24 loss = 8.2984e-01
epoch 25 loss = 8.2983e-01
epoch 26 loss = 8.2982e-01
epoch 27 loss = 8.2982e-01
epoch 28 loss = 8.2982e-01
epoch 29 loss = 8.2981e-01
epoch 30 loss = 8.2979e-01
epoch 31 loss = 8.2978e-01
epoch 32 loss = 8.2977e-01
epoch 33 loss = 8.2977e-01
epoch 34 loss = 8.2976e-01
epoch 35 loss = 8.2976e-01
epoch 36 loss = 8.2976e-01
epoch 37 loss = 8.2976e-01
epoch 38 loss = 8.2976e-01
epoch 39 loss = 8.2975e-01
epoch 40 loss = 8.2975e-01
epoch 41 loss = 8.2975e-01
epoch 42 loss = 8.2975e-01
epoch 43 loss = 8.2975e-01
epoch 44 loss = 8.2975e-01
epoch 45 loss = 8.2975e-01
epoch 46 loss = 8.2975e-01
epoch 47 loss = 8.2974e-01
epoch 48 loss = 8.2974e-01
epoch 49 loss = 8.2974e-01
epoch 50 loss = 8.2974e-01
epoch 51 loss = 8.2974e-01
epoch 52 loss = 8.2974e-01
epoch 53 loss = 8.2974e-01
epoch 54 loss = 8.2974e-01
epoch 55 loss = 8.2974e-01
epoch 56 loss = 8.2973e-01
epoch 57 loss = 8.2973e-01
epoch 58 loss = 8.2973e-01
epoch 59 loss = 8.2972e-01
epoch 60 loss = 8.2972e-01
epoch 61 loss = 8.2972e-01
epoch 62 loss = 8.2972e-01
epoch 63 loss = 8.2972e-01
epoch 64 loss = 8.2972e-01
epoch 65 loss = 8.2972e-01
epoch 66 loss = 8.2972e-01
epoch 67 loss = 8.2972e-01
epoch 68 loss = 8.2972e-01
epoch 69 loss = 8.2972e-01
*************** END FIRST PHASE ***************
* Training time: 11.961927890777588s
* Average epoch time: 0.17336127377938534s
***************** END TRAINING ****************
* Training time: 67.57927632331848s
print("LBFGS")
print("u = ", rh_adapt_error_u[:])
print("v = ", rh_adapt_error_v[:])
print("s = ", rh_adapt_error_stress[:])
print("s max = ", rh_adapt_error_stress_max[:])
LBFGS
u = [0.1613041 0.05543183 0.02019657 0.00428556 0.0009745 ]
v = [0.02091463 0.00601936 0.0020075 0.00044092 0.00012973]
s = [0.20231483 0.09395327 0.06183964 0.03117634 0.02013201]
s max = [0.85569806 0.97563867 1.00978807 1.00608271 0.98011422]
# Plot normalized displacement error
fig = matplotlib.pyplot.gcf()
ax = plt.gca()
print(rh_adapt_error_u.shape)
plt.plot(mesh_resolution, r_adapt_error_u[:,1],'--', color = "darkblue", label = r'$\| e_{u_x}\|_2$'+ ", r-adaptivity")
plt.plot(mesh_resolution, r_adapt_error_v[:,1],'--', color = "purple", label = r'$\| e_{u_y}\|_2$'+ ", r-adaptivity")
plt.plot(mesh_resolution, r_adapt_error_stress[:,1],'--', color = "green", label = r'$\| e_{\sigma_{VM}}\|_2$'+ ", r-adaptivity")
plt.plot(mesh_resolution, rh_adapt_error_u,':', color = "darkblue", label = r'$\| e_{u_x}\|_2$'+ ", rh-adaptivity")
plt.plot(mesh_resolution, rh_adapt_error_v,':', color = "purple", label = r'$\| e_{u_y}\|_2$'+ ", rh-adaptivity")
plt.plot(mesh_resolution, rh_adapt_error_stress,':', color = "green", label = r'$\| e_{\sigma_{VM}}\|_2$'+ ", rh-adaptivity")
ax.set_xscale('log')
ax.set_yscale('log')
ax.set_ylim([0.00006, 0.3])
plt.xlabel("Number of mesh nodes")
plt.ylabel("Normalized displacement error")
plt.legend(loc='center left', bbox_to_anchor=(1, 0.5),frameon=False )
plt.show()
# Plot maximal stress
fig = matplotlib.pyplot.gcf()
ax = plt.gca()
plt.plot(mesh_resolution, error_stress_max[:,1],'-', color = "lightblue", label = "fixed mesh")
plt.plot(mesh_resolution, r_adapt_error_stress_max[:,1],'--', color = "lightblue", label = "r-adaptivity")
plt.plot(mesh_resolution, rh_adapt_error_stress_max,':', color = "lightblue", label = "rh-adaptivity")
ax.set_xscale('log')
ax.set_yscale('log')
plt.xlabel("Number of mesh nodes")
# plt.ylabel(r'$\sigma^{max}_{VM}$')
plt.ylabel(r'$\frac{\sigma_{VM}^{max}}{\sigma_{VM, ref}^{max}}$')
plt.legend(loc='center left', bbox_to_anchor=(1, 0.5),frameon=False )
plt.show()
(5,)
