Upcoming Event: Oden Institute Seminar

Neural PDE: AI-Enhanced Physics Simulation

Peter Yichen Chen, Assistant Professor, University of British Columbia

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 incoming 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. Peter’s research advances 3D content creation for artists, design/fabrication/control for engineers, and material discovery for scientists. His interdisciplinary work spans computer graphics, machine learning, scientific computing, mechanics, and robotics.