Physics simulation has wide applications in computer graphics, mechanical engineering, and robotics. Particularly, differentiable physics simulation has become increasingly popular as the calculation of gradients allows for richer analysis of a physics system. However, deriving gradients in a simulator is labor-intensive and error-prone. Existing autodiff tools such as Pytorch are not designed for data structures used in physics simulation. My goal is to develop an autodiff tool specialized for differentiable simulation. Specifically, I will focus on automatic calculation of higher-order derivatives and differentiating implicit functions. With the tools I develop, I hope to unlock downstream applications in physics simulation, controller design, and inverse problems.

I am participating in SuperUROP to gain research experience in the field of computer graphics. I look forward to applying what I learned in 6.837 and 6.839 and contributing to the research in differentiable physics simulation. The project is exciting as it intersects my interest in computer graphics, programming, and physics. I hope to deepen my understanding in all these fields as well as improve my research and communication skills.