AI4Science: Learning and solving PDE

Introduction

AI for Science is promoting the transformation of scientific research paradigm, which has a great impact on the research of forward and inverse problems related to partial differential equation models describing various natural and social phenomenon. The main content of this course is to explain the literature related to "machine learning and differential equations" in recent years, including machine learning-based methods for solving PDE forward and inverse problems and dynamical system modeling, numerical examples, and code. Meanwhile, audiences studying on ML&XDE are encouraged to share your research work.

Lecturer Intro

Fansheng Xiong (熊繁升) is currently an Assistant Researcher Fellow of BIMSA. Before that, he got a bachelor's degree from China University of Geosciences (Beijing), and a doctoral degree from Tsinghua University. He was a visiting student at Yale University for one year. His research interest mainly focuses on solving PDE-related forward/inverse problems based on machine learning algorithms (DNN, PINN, DeepONet, etc.), and their applications in geophysical wave propagation problems and turbulence modeling of fluid mechanics. The relevant efforts have been published in journals such as JGR Solid Earth, GJI, Geophysics, etc.