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.
Automated derivation of the adjoint of high-level transient finite element programs.SIAM Journal on Scientific Computing, 35(4):C369–C393
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A framework learns constitutive priors from noisy data to enable PDE-constrained inverse design of elastic networks using latent variables, homotopy continuation, Chamfer distance matching, and neural smoothness constraints.
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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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Constitutive Priors for Inverse Design
A framework learns constitutive priors from noisy data to enable PDE-constrained inverse design of elastic networks using latent variables, homotopy continuation, Chamfer distance matching, and neural smoothness constraints.