Physics-augmented neural networks act as stable, thermodynamically consistent surrogates for microscale problems, enabling simultaneous optimization of macroscale material layout and microscale descriptors in nonlinear finite-strain anisotropic hyperelastic structures.
Learning hyperelastic anisotropy from data via a tensor basis neural network.Journal of the Mechanics and Physics of Solids, 168:105022, 2022
2 Pith papers cite this work. Polarity classification is still indexing.
2
Pith papers citing it
years
2026 2verdicts
UNVERDICTED 2representative citing papers
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.
citing papers explorer
-
Multiscale topology optimization of compressible and nearly incompressible anisotropic hyperelastic structures using physics-augmented neural networks
Physics-augmented neural networks act as stable, thermodynamically consistent surrogates for microscale problems, enabling simultaneous optimization of macroscale material layout and microscale descriptors in nonlinear finite-strain anisotropic hyperelastic structures.
-
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.