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.
Journal of the Mechanics and Physics of Solids 153, 104474
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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.
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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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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.