An adaptive database and iterative pattern recognition algorithm lets Material Fingerprinting discover arbitrary linear combinations of polyconvex isotropic and anisotropic hyperelastic features from experimental data.
Computer Methods in Applied Mechanics and Engineering 428, 117063
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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.
A thermodynamics-constrained ML framework learns robust, consistent constitutive models for inelastic materials from macroscopic stress-strain data and generalizes to unseen paths.
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Adaptive Material Fingerprinting for the fast discovery of polyconvex feature combinations in isotropic and anisotropic hyperelasticity
An adaptive database and iterative pattern recognition algorithm lets Material Fingerprinting discover arbitrary linear combinations of polyconvex isotropic and anisotropic hyperelastic features from 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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Learning inelastic constitutive models from stress-strain data under hard thermodynamic constraints
A thermodynamics-constrained ML framework learns robust, consistent constitutive models for inelastic materials from macroscopic stress-strain data and generalizes to unseen paths.