讲座时间：2026年4月22日 16:15-17:45
讲座地点：雁栖湖校区西区 教3-219
讲座题目：Shallow and Deep Neural Network Approximations
讲座嘉宾：
N. Sukumar is a professor at the University of California, Davis, in the U.S. Sukumar has published 105 peer-reviewed articles in major international journals with 17650 citations, leading to an h-index of 53 (Source: Google Scholar). His research foci over the past decade has been on novel discretization methods (such as virtual element methods) on polytopal meshes, smooth maximum entropy approximation schemes, construction of high-order cubature rules over polytopes and curved geometries, and new methods development to solve the Kohn-Sham equations of density functional theory. A recent emphasis is on applying deep learning to solve partial differential equations over complex geometries.
讲座内容：
In scientific machine learning (SciML), physics-informed neural networks has become a powerful and emerging construct to solve systems of ordinary and partial differential equations. Underlying SciML are neural networks, which are nonlinear approximations. In this lecture, I will introduce the essential concepts that underlie the building blocks of neural networks. To solve differential equations with PINNs, the use of shallow and deep neural networks (fully-connected feedforward multilayer perceptron, MLP) will be discussed.
