Recent progress in LLMs has transformed text and code generation, yet models still falter on PDEs (partial differential equation) where correctness, constraints, and physical consequences are critical. This talk explores how formal LLM reasoning can advance symbolic PDE modeling. First, our PDE-Controller formalizes informal PDEs, synthesizes solver-ready code, and plans subgoals to tackle nonconvex control via interactions with external solvers. Second, our Lean Finder accelerates PDE formalization with a semantics-aware search engine for Lean/Mathlib that retrieves relevant theorems, outperforming GPT models and earning strong reception in the AI-for-math community. Together, these efforts aim to close the loop between automated reasoning and human heuristics across diverse, verifiable scientific tasks.
Verifiable PDE Reasoning and Modeling with Neurosymbolics
Wuyang Chen, Simon Fraser University, Assistant Professor
Bio
Dr. Wuyang Chen is a tenure-track Assistant Professor in Computing Science at Simon Fraser University. Previously, he was a postdoctoral researcher in Statistics at the University of California, Berkeley. He obtained his Ph.D. in Electrical and Computer Engineering from the University of Texas at Austin in 2023. Dr. Chen's research focuses on integrating AI methods with physical knowledge, scientific machine learning, and theoretical understanding of deep networks. Dr. Chen has published papers at CVPR, ECCV, ICLR, ICML, NeurIPS, and other top conferences. Dr. Chen’s research has been recognized by the US NSF newsletter, two Doctoral Dissertation Awards from INNS and iSchools, AAAI New Faculty Highlights, and Nvidia Academic Grant Award. Dr. Chen also hosted and co-organized many conference workshops at NeurIPS, ICLR, CVPR.