报告题目:Mix-training physics-informed neural networks for the rogue waves of nonlinear Schrodinger equation

报告人：李彪教授（宁波大学）

报告时间：2023年3月6日 （周一）14:00-15:00

报告地点：广B208

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.

报告人简介：李彪，宁波大学数学与统计学院教授，浙江省151人才工程”（第三层次）、宁波市“4321” 人才工程（第二层次）。主要从事数学物理，Lie群及其在微分方程中的应用，数学机械化等领域的研究工作。已在SCI系统发表学术论文100余篇，发表论文已被SCI他引1000多次。主持完成国家自然科学基金3项，中国博士后基金1项，浙江省自然科学基金2项。参与完成国家自然科学基金和省、市自然科学基金多项。现参加国家自然科学基金重点项目一项，主持国家自然科学基金面上1项。

邀请人：沈守枫