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.
URL:http://arxiv.org/abs/2212.07723
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Adaptive Material Fingerprinting builds hyperelastic material models as greedy linear combinations of precomputed feature fingerprints, reaching neural-network-level fit accuracy on rubber and skin without online optimization.
citing papers explorer
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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.
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Adaptive Material Fingerprinting for the fast discovery of polyconvex feature combinations in isotropic and anisotropic hyperelasticity
Adaptive Material Fingerprinting builds hyperelastic material models as greedy linear combinations of precomputed feature fingerprints, reaching neural-network-level fit accuracy on rubber and skin without online optimization.