Date：2025/7/21 10:30-11:40
Location：R101, CSIE
Speakers：Prof. Peter Yichen Chen
Host：李濬屹
Abstract:
Physics simulation has become the third pillar of science and engineering, alongside theory and experiments. Two distinct simulation paradigms have emerged: the classical laws of physics approach, e.g., leveraging partial differential equations (PDEs) derived from first principles, and the data-driven approach, e.g., training neural networks from observations. My research asks: how can we effectively merge these two approaches to amplify their respective strengths? In this talk, I will show that by organically integrating these two approaches, we can create physics simulations that significantly outperform classical physics-only approaches in terms of (1) accuracy, (2) speed, and (3) accessibility. Simultaneously, our hybrid physics-data simulations possess exceptional generalization capabilities, which, unlike their pure data-driven counterparts, carefully incorporate PDEs as an inductive bias.
Biography:
Peter Yichen Chen is an assistant professor at the University of British Columbia, where he directs the UBC PhysAI Lab. He was a postdoc at MIT CSAIL and earned his CS PhD from Columbia University. Earlier, he was a Sherwood Prize–winning math undergrad at UCLA. His research advances 3D content creation for artists, design/fabrication/control for engineers, and material discovery for scientists. Peter’s interdisciplinary work spans computer graphics, machine learning, scientific computing, mechanics, and robotics, and his co-authored papers have been recognized with several awards, including a SIGGRAPH Best Paper Award.