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.
Linear convergence in the approximation of rank-one convex envelopes
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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.