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.
A mechanics-informed artificial neural network approach in data-driven constitutive modeling.International Journal for Numerical Methods in Engineering, 123 20 (12):2738–2759
3 Pith papers cite this work. Polarity classification is still indexing.
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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.
Input-convex neural networks in elementary polynomials of signed singular values provably approximate any frame-indifferent isotropic polyconvex hyperelastic energy.
citing papers explorer
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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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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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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.