报告人：Iowa State University, USA  刘海亮（Hailiang LIU）  professor

报告时间：2023年7月11日15：30-17：30

报告地点：河南省大数据研究院大会议室

Abstract：

We will present a partial differential equation framework for deep residual neural networks and for the associated learning problem. This is done by carrying out the continuum limits of neural networks with respect to width and depth. We study the wellposedness of the forward problem, and establish several optimal conditions for the inverse deep learning problem. This talk concerns several mathematical aspects of deep learning and the use of optimal control tools in solving the learning problem. This presentation is based on a joint work with Peter Markowich (KAUST).

简介：

Hailiang Liu is a Professor of Mathematics and Computer Science at the Iowa State University (ISU). He earned his Bachelor degree from Henan Normal University, Master degree from Tsinghua University, and Ph.D. degree from the Chinese Academy of Sciences, all in Mathematics. His research interests include analysis of partial differential equations, the development of high order numerical algorithms for solving these PDE problems, with diverse applications. He is the recipient of many awards and honors, including the Alexander von Humboldt-Research Fellowship, and the inaugural Holl Chair in Applied Mathematics. He is the author of over 160 peer reviewed papers. His recent work has focused on the study of data-driven deep learning problems.