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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The paper introduces penalty-based and randomized-exploration adaptations to flow matching for improved constraint satisfaction in generative models while matching target distributions.
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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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Constraint-Aware Flow Matching via Randomized Exploration
The paper introduces penalty-based and randomized-exploration adaptations to flow matching for improved constraint satisfaction in generative models while matching target distributions.