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.
Tensor basis gaussian process models of hyperelastic materials
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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.
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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.
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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.