Input-convex neural networks in elementary polynomials of signed singular values provably approximate any frame-indifferent isotropic polyconvex hyperelastic energy.
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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.
The paper is a memorial tribute collecting reminiscences of Robert V. Kohn's exemplary life and contributions to mathematics.
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Robert V. Kohn (1953-2026)
The paper is a memorial tribute collecting reminiscences of Robert V. Kohn's exemplary life and contributions to mathematics.