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.
Journal of the Mechanics and Physics of Solids , volume =
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A variational framework models SiO2 glass under pressure as binary phase coexistence and matches experimental elastic moduli and volume changes.
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Compression of Polyconvex Envelopes of Isotropic Functions via Monotonic Input Convex Neural Networks
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.
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On a variational model for phase transformation in SiO2 glass
A variational framework models SiO2 glass under pressure as binary phase coexistence and matches experimental elastic moduli and volume changes.