报告题目：Weak Adversarial Networks (WAN): A Computational Method for High-dimensional Forward and Inverse Problems

报告时间：2021年7月5日，星期一，上午9:00-10:30

腾讯会议号： 327 379 927

报告人：  周好民教授，美国佐治亚理工学院

报告摘要：

Solving general high-dimensional forward and inverse problems involving partial differential equations (PDE) is a long-standing challenge in numerical mathematics. In this talk, we present a novel approach by leveraging their weak formulations and neural networks. We convert the problem of finding the weak solution of forward and inverse problems into an operator norm minimization problem induced from the weak formulation. The weak solution, unknown coefficient, and the test function are then parameterized as the primal and adversarial networks respectively, which are alternately updated to approximate the optimal network parameter setting. We apply our method to a variety of test problems to demonstrate its promising performance. This presentation is based on joint work with Gang Bao (Zhejiang University), Xiaojing Ye (Georgia State) and Yaohua Zang (Zhejiang University).

报告人简介：

Haomin Zhou is a professor in the School of Mathematics at Georgia Institute of Technology. He received his B.S. in pure mathematics from Peking University, M.Phil in applied mathematics from the Chinese University of Hong Kong, and Ph.D. in applied mathematics from University of California, Los Angeles in 1991, 1996 and 2000 respectively. He spent 3 years in California Institute of Technology as a postdoctoral scholar and von Karman instructor, before he joined Georgia Institute of Technology as an assistant professor in 2003. His research interests are on numerical analysis and scientific computing, specialized in PDE and wavelet techniques in image processing, numerical methods for stochastic differential equations, and discrete optimal transport. He is a recipient of the NSF CAREER AWARD in applied and computational mathematics in 2007, and Feng Kang prize in scientific computing in 2019.

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会议时间：2021/07/05 09:00-11:00 (GMT+08:00) 中国标准时间 - 北京

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会议 ID：327 379 927