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.
K., Fern´ andez, M., Martin, R
5 Pith papers cite this work, alongside 17 external citations. Polarity classification is still indexing.
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Pith papers citing it
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representative citing papers
A causal energetic neural network framework learns thermodynamically consistent history-dependent constitutive laws, proving internal variables are unique up to linear transformation and achieving 2% error on polycrystalline magnesium data.
Input-convex neural networks in elementary polynomials of signed singular values provably approximate any frame-indifferent isotropic polyconvex hyperelastic energy.
A monotonic ICNN architecture with domain reduction to the positive octant approximates polyconvex envelopes of isotropic functions more efficiently than existing necessary-and-sufficient methods, demonstrated on Saint Venant-Kirchhoff energy.
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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A Neural-Network Framework to Learn History-Dependent Constitutive Laws and Identifiability of Internal Variables
A causal energetic neural network framework learns thermodynamically consistent history-dependent constitutive laws, proving internal variables are unique up to linear transformation and achieving 2% error on polycrystalline magnesium data.
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Input convex neural networks: universal approximation theorem and implementation for isotropic polyconvex hyperelastic energies
Input-convex neural networks in elementary polynomials of signed singular values provably approximate any frame-indifferent isotropic polyconvex hyperelastic energy.
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Compression of Polyconvex Envelopes of Isotropic Functions via Monotonic Input Convex Neural Networks
A monotonic ICNN architecture with domain reduction to the positive octant approximates polyconvex envelopes of isotropic functions more efficiently than existing necessary-and-sufficient methods, demonstrated on Saint Venant-Kirchhoff energy.
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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.