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.
A new family of constitutive artificial neural networks towards automated model discovery.Computer Methods in Applied Mechanics and Engineering, 403:115731
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Input-convex networks learn thermodynamically admissible internal energy and dissipation potentials for isotropic thermoelasticity from temperature and deformation data without entropy measurements.
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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A Convex Route to Thermoelasticity: Learning Internal Energy and Dissipation
Input-convex networks learn thermodynamically admissible internal energy and dissipation potentials for isotropic thermoelasticity from temperature and deformation data without entropy measurements.
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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.