An end-to-end differentiable co-optimization method uses implicit neural representations of geometry together with a JAX multiphysics solver to jointly tune shape, material state, and boundary conditions over transient rollouts, shown on a hamburger-cooking benchmark.
Structural optimization using sensitivity analysis and a level-set method.Journal of Computational Physics, 194(1):363– 393, 2004
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Differentiable Multiphysics Co-Optimization via Implicit Neural Representations: A Transient Hamburger-Cooking Benchmark
An end-to-end differentiable co-optimization method uses implicit neural representations of geometry together with a JAX multiphysics solver to jointly tune shape, material state, and boundary conditions over transient rollouts, shown on a hamburger-cooking benchmark.