报告时间：2021年6月4日10:00-11:00
报告地点：红瓦楼726
报告人简介：黄建国，上海交通大学数学科学学院长聘教授、博士生导师。1986年本科毕业于复旦大学数学系，1992年在复旦大学数学所获理学博士学位。2006年获教育部新世纪优秀人才称号，2016年荣获上海市育才奖，2019年在第八届世界华人数学家大会作邀请报告（45分钟）。现任中国仿真学会不确定性系统分析与仿真专业委员会委员，中国计算数学学会下设有限元方法工作小组成员，也是知名学术《Communications on Applied Mathematics and Computation》和《数值计算与计算机应用》编委。主要从事有限元方法（含DG方法和虚拟元方法）与应用，快速算法设计与分析和人工智能算法设计与应用的研究工作，共发表学术论文100余篇，部分在计算与应用数学方面的顶级学术刊物如SIAM系列，Math. Comp.，Numer. Math., JCP，Inverse Problems和JDE等发表。先后7次主持国家自然科学基金项目，参加973项目和上海市重点项目各1项。
报告摘要：
In this talk, I will introduce and study a deep learning method for solving an elliptic hemivariational inequality (HVI). In this method, an expectation minimization problem is first formulated based on the variational principle of underlying HVI, which is solved by stochastic optimization algorithms using three different training strategies for updating network parameters. The method is applied to solve two practical problems in contact mechanics, one of which is a frictional bilateral contact problem and the other of which is a frictionless normal compliance contact problem. Numerical results show that the deep learning method is efficient in solving HVIs and the adaptive mesh-free multigrid algorithm can provide the most accurate solution among the three learning methods.This is a joint work with Chunmei Wang from Texas Tech University and Haoqin Wang from SJTU.