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Seminar on Control Theory and Nonlinear Filtering
Learning multiple-input operators via tensor product
Learning multiple-input operators via tensor product
Organizer
Speaker
Time
Friday, October 27, 2023 8:30 PM - 9:00 PM
Venue
Online
Abstract
In this talk, we will share a work about the neural operator. Basically, neural operators aim to learn operators, via neural networks, that map between infinite-dimensional function spaces. So far, all the existing neural operators are only designed to learn operators defined on a single Banach space. In the presented paper from Jin et al, multiple-input operators are considered called MIONet which can deal with multiple input problems.
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.