Speaker:

Zongyi Li

Talk Title:

Neural Operator for Scientific Computing

Date and Location:

Tuesday, February 18, 2025

Bahen Centre for Information Technology, BA 3200

This lecture is open to the public. No registration is required, but space is limited.

Abstract:

Scientific computing, which aims to accurately simulate complex physical phenomena, often requires substantial computational resources. By viewing data as continuous functions, we leverage the smoothness structures of function spaces to enable efficient large-scale simulations. We introduce the neural operator, a machine learning framework designed to approximate solution operators in infinite-dimensional spaces, achieving scalable physical simulations across diverse resolutions and geometries. Beginning with the Fourier Neural Operator, we explore recent advancements including scale-consistent learning techniques and adaptive mesh methods.  We demonstrate real-world applications in weather prediction, carbon capture, and plasma dynamics, achieving speedups of several orders of magnitude.

About Zongyi Li:

Zongyi Li is a final-year PhD candidate in Computing + Mathematical Sciences at Caltech, working with Prof. Anima Anandkumar and Prof. Andrew Stuart. His research focuses on developing neural operator methods for accelerating scientific simulations. He has completed three summer internships at Nvidia (2022-2024). Zongyi received his undergraduate degrees in Computer Science and Mathematics from Washington University in St. Louis (2015-2019). His research has been supported by the Kortschak Scholarship, PIMCO Fellowship, Amazon AI4Science Fellowship, and Nvidia Fellowship.