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.
Review of physics-informed neural networks: Challenges in loss function design and geometric integration.Mathematics, 13(20):3289, 2025
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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.