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.
Polyconvex anisotropic hyperelasticity with neural networks.Journal of the Mechanics and Physics of Solids, 159:104703
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Conformal quantile regression endows existing neural constitutive models with distribution-free probabilistic predictions for anisotropic soft tissues while preserving thermodynamic consistency via a polyconvex strain-invariant formulation.
paFEMU enables rapid constitutive model discovery by integrating sparse regression, physics augmentation, and finite element adjoint optimization on multi-modal data for interpretable transfer learning.
citing papers explorer
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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.
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Conformal Quantile Regression for Neural Probabilistic Constitutive Modeling
Conformal quantile regression endows existing neural constitutive models with distribution-free probabilistic predictions for anisotropic soft tissues while preserving thermodynamic consistency via a polyconvex strain-invariant formulation.
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Towards Rapid Constitutive Model Discovery from Multi-Modal Data: Physics Augmented Finite Element Model Updating (paFEMU)
paFEMU enables rapid constitutive model discovery by integrating sparse regression, physics augmentation, and finite element adjoint optimization on multi-modal data for interpretable transfer learning.