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.
JAX-FEM: A differentiable GPU-accelerated 3d finite element solver for automatic inverse design and mechanistic data science.Computer Physics Communications, 291:108802
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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.