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Seminar on Control Theory and Nonlinear Filtering
An expert's guide to training physics-informed neural networks
An expert's guide to training physics-informed neural networks
Organizer
Speaker
Time
Monday, August 28, 2023 2:30 PM - 3:00 PM
Venue
数学系理科楼A-203
Abstract
Physics-informed neural networks (PINNs) have been popularized as a deep learning framework that can seamlessly synthesize observational data and partial differential equation (PDE) constraints. Their practical effectiveness however can be hampered by training pathologies, but also oftentimes by poor choices made by users who lack deep learning expertise. In this talk, we will present a literature about practices that can significantly improve the training efficiency and overall accuracy of PINNs.
Speaker Intro
Xiaopei Jiao received his bachelor's degree from the Zhiyuan College of Shanghai Jiao Tong University and his Ph.D. from the Department of Mathematical Sciences at Tsinghua University. He subsequently worked as a postdoctoral researcher at the Beijing Institute of Mathematical Sciences and Applications (BIMSA) and at the University of Twente in the Netherlands. His current research interests include finite-dimensional filtering theory, Yau-Yau filtering methods, physics-informed neural networks, and bioinformatics. His research focuses primarily on: (1) using geometric tools such as Lie algebras for solving partial differential equations and classifying nonlinear systems; (2) designing novel numerical algorithms based on physics-informed neural networks.