FE-MAD is an end-to-end differentiable FEM framework that learns polyconvex and interpretable constitutive neural networks for incompressible hyperelasticity directly from DIC and heterogeneous experimental data.
Mechanical mnist: A benchmark dataset for mechanical metamodels.Extreme Mechanics Letters, 36:100659, 2020
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Finite Element-Based Material Learning via Automatic Differentiation: Learning constitutive neural network models from full-field deformation data
FE-MAD is an end-to-end differentiable FEM framework that learns polyconvex and interpretable constitutive neural networks for incompressible hyperelasticity directly from DIC and heterogeneous experimental data.