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 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.
SAGMTL decomposes dynamic sparse OD demand prediction into joint structural state modeling and flow intensity estimation via node-edge collaborative graph representations.
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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.