Mix-training physics-informed neural networks for the rogue waves of nonlinear Schrodinger equation
数学公众报告(120周年校庆系列第69场)
报告题目(Title)：Mix-training physics-informed neural networks for the rogue waves of nonlinear Schrodinger equation
报告人(Speaker)：李彪 教授（宁波大学数学与统计学院）
地点(Place)：腾讯会议ID：369 417 328
时间(Time)：2022年12月30日(周五), 14:00-15:00
邀请人(Inviter)：郭来刚、王灯山
报告摘要
In this work, we propose Mix-training physics-informed neural networks (PINNs), a deep learning model with more approximation ability based on PINNs, combined with mixed training and prior information. We demonstrate the advantages of this model by exploring rogue waves with rich dynamic behavior in the nonlinear Schr¨odinger (NLS) equation. Numerical results show that compared with the original PINNs, this model can not only quickly recover the dynamical behavior of the rogue waves of NLS equation, but also significantly improve its approximation ability and absolute error accuracy, the prediction accuracy improved by two to three orders of magnitude. In particular, when the space-time domain of the solution expands or the solution has a local sharp region, the proposed model still has high prediction accuracy.
主讲人简介
李彪，宁波大学数学与统计学院教授，博士生导师。主要从事可积系统及应用、机器学习、黎曼希尔伯特问题等研究。主持完成国家自然科学基金4项、省部级项目3项；参与完成国家自然科学基金重点项目2项；现主持国家自然科学基金面上项目1项和参与国家自然科学基金重点项目1项。发表SCI论文100余篇，他引3000余次。
