Differentiable Simulator

Differentiable simulators are computational models that allow for the calculation of gradients of simulated physical processes, enabling efficient gradient-based optimization within machine learning frameworks. Current research focuses on applying these simulators to diverse areas, including robotics (e.g., locomotion control, manipulation), computational physics (e.g., fluid dynamics, electromagnetism), and inverse problems, often employing neural networks and graph-based methods to improve efficiency and generalization. This capability significantly accelerates the training of controllers and the solution of complex optimization problems, impacting fields ranging from autonomous vehicle design to material science.