A differentiable physics framework can mathematically optimize the shape and cooking temperature of a hamburger to ensure perfectly even heating.
Complex materials like meat change their physical properties as they cook, making them difficult to model with traditional tools. This framework uses implicit neural representations to co-optimize both the geometry of the food and the heat applied to it. It proves that high-end AI and physics solvers can solve mundane but variable problems in real-world manufacturing. The same logic applies to any process where a changing material must reach a specific state. Engineering perfect results for complex organic materials is now a differentiable math problem.
Differentiable Multiphysics Co-Optimization via Implicit Neural Representations: A Transient Hamburger-Cooking Benchmark
arXiv · 2605.01040
The co-optimization of geometry and physical parameters remains challenging in transient multiphysics systems involving moving boundaries, nonlinear material response, phase transitions, and competing objectives. Existing methods often optimize geometry and physical variables separately, rely on simplified steady-state physics, or require offline data generation and reduced design spaces. Here, we present an end-to-end differentiable co-optimization framework that couples an implicit neural repr