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Constitutive modeling on hyperelasticity (test)

1.094Relative L2 ErroreDAFNO
-2.7889223.4207949.630575.84021Apr 30, 2023

Evaluation results

MethodLinks
eDAFNO# of training samples=10002023.041.094
iDAFNO# of training samples=10002023.041.207
FNO w/ smooth χ# of training samples=10002023.041.415
IFNO w/ smooth χ# of training samples=10002023.041.509
Geo-FNO# of training samples=10002023.042.316
F-FNO# of training samples=10002023.043.193
iDAFNO# of training samples=1002023.044.214
eDAFNO# of training samples=1002023.044.247
IFNO w/ mask# of training samples=10002023.044.481
FNO w/ mask# of training samples=10002023.044.55
IFNO w/ smooth χ# of training samples=1002023.045.088
UNet# of training samples=10002023.045.462
FNO w/ smooth χ# of training samples=1002023.045.479
IFNO w/ mask# of training samples=1002023.047.7
FNO w/ mask# of training samples=1002023.047.852
Geo-FNO# of training samples=1002023.0410.343
DeepONet# of training samples=10002023.0411.998
F-FNO# of training samples=1002023.0412.135
GNO# of training samples=10002023.0413.007
eDAFNO# of training samples=102023.0416.446
iDAFNO# of training samples=102023.0416.669
IFNO w/ smooth χ# of training samples=102023.0417.145
FNO w/ smooth χ# of training samples=102023.0417.431
GNO# of training samples=1002023.0418.574
IFNO w/ mask# of training samples=102023.0419.262
FNO w/ mask# of training samples=102023.0419.487
DeepONet# of training samples=1002023.0425.455
Geo-FNO# of training samples=102023.0428.725
GNO# of training samples=102023.0429.305
UNet# of training samples=1002023.0434.467
DeepONet# of training samples=102023.0435.334
F-FNO# of training samples=102023.0435.672
UNet# of training samples=102023.0498.167