Differentiable physical simulations are increasingly essential tools in modern intelligent systems. Among various differentiable solvers, the Material Point Method (MPM) stands out due to its inherent differentiability. One of its key advantages over alternative differentiable simulators is its adeptness at handling topologically dynamic solid and fluid materials, as well as managing frictional contact. In this talk, we will explore recent contributions from my research group on leveraging differentiable MPM for multiple applications. These range from training controllers for soft robotic locomotion to optimizing the energy-efficient design of 3D-printed unmanned aerial vehicles, and reconstructing the geometry and physical properties of diverse materials from video footage.
Chenfanfu Jiang is an associate professor of Mathematics at UCLA, leading diverse research projects at the UCLA Multi-Physics Lagrangian-Eulerian Simulations (MultiPLES) Laboratory. He received his Ph.D. from UCLA in 2015. He has published over 100 research papers in the realms of scientific computing, physics-based simulation, computer graphics, machine learning, and robotics. His notable contributions have been the development of innovative algorithms focusing on the Material Point Method (MPM) and the Incremental Potential Contact (IPC) method. He was awarded the UCLA Edward K. Rice Outstanding Doctoral Student Award (2015), the NSF CRII award (2018), the NSF CAREER award (2020), the Sony Faculty Innovation Award (2023), as well as SCA, MIG and ICRA best paper awards.