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Seminar on Control Theory and Nonlinear Filtering
Correct the misspecified physics model by using the physics informed neural network
Correct the misspecified physics model by using the physics informed neural network
Organizer
Speaker
Time
Wednesday, October 9, 2024 8:30 PM - 9:00 PM
Venue
Online
Abstract
In many applied and engineering scenarios, prior of model is not fully understood. In this talk, we shall discuss how to correct physics model by applying noisy data. PINN, as a popular deep learning technique, has great advantages to handle hybrid problem. Here the key point is to choose suitable ansatz of solution and unknown physics term.
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.