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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3 Pith papers cite this work. Polarity classification is still indexing.
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2026 3verdicts
CONDITIONAL 3representative citing papers
CSSV-NNs and inc-CSSV-NNs provide universal approximation of frame-indifferent isotropic polyconvex hyperelastic energies, showing Ball's criterion is sufficient but not necessary.
Variational system identification infers material parameters for neo-Hookean, modified HGO, and reduced polynomial models from full-volume tendon strain data, with the modified HGO and three-term polynomial capturing key intact and injured behaviors better than neo-Hookean.
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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Modeling isotropic polyconvex hyperelasticity by neural networks -- sufficient and necessary criteria for compressible and incompressible materials
CSSV-NNs and inc-CSSV-NNs provide universal approximation of frame-indifferent isotropic polyconvex hyperelastic energies, showing Ball's criterion is sufficient but not necessary.
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Constitutive parameter inference using physics-based data-driven modeling in full volume datasets of intact and torn rotator cuff tendons
Variational system identification infers material parameters for neo-Hookean, modified HGO, and reduced polynomial models from full-volume tendon strain data, with the modified HGO and three-term polynomial capturing key intact and injured behaviors better than neo-Hookean.