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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Input-convex neural networks in elementary polynomials of signed singular values provably approximate any frame-indifferent isotropic polyconvex hyperelastic energy.
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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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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.