Polyconvexity implies true-stress-true-strain monotonicity in incompressible isotropic hyperelasticity, which is enforced in four PANN architectures that show varying extrapolation behavior on experimental data.
Computer Methods in Ap- plied Mechanics and Engineering 437, 117725
3 Pith papers cite this work, alongside 32 external citations. Polarity classification is still indexing.
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EquiNO with Q-DEIM creates reduced-order physics-informed surrogates for 3D hyperelastic RVEs that enforce equilibrium and periodicity by construction, achieve 10^3 speedups, and accurately interpolate and extrapolate stresses from few snapshots.
Adaptive Material Fingerprinting builds hyperelastic material models as greedy linear combinations of precomputed feature fingerprints, reaching neural-network-level fit accuracy on rubber and skin without online optimization.
citing papers explorer
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Concurrent enforcement of polyconvexity and true-stress-true-strain monotonicity in incompressible isotropic hyperelasticity: application to neural network constitutive models
Polyconvexity implies true-stress-true-strain monotonicity in incompressible isotropic hyperelasticity, which is enforced in four PANN architectures that show varying extrapolation behavior on experimental data.
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Physics-Informed Reduced-Order Operator Learning for Hyperelasticity in Continuum Micromechanics
EquiNO with Q-DEIM creates reduced-order physics-informed surrogates for 3D hyperelastic RVEs that enforce equilibrium and periodicity by construction, achieve 10^3 speedups, and accurately interpolate and extrapolate stresses from few snapshots.
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Adaptive Material Fingerprinting for the fast discovery of polyconvex feature combinations in isotropic and anisotropic hyperelasticity
Adaptive Material Fingerprinting builds hyperelastic material models as greedy linear combinations of precomputed feature fingerprints, reaching neural-network-level fit accuracy on rubber and skin without online optimization.