报告问题 (Title)：基于物理学知识的神经网络混淆训练追求非线性薛定谔方程怪波解（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）

报告时间 (Time)：2023年5月12日(周五) 16:00

报告所在 (Place)：腾讯聚会：586 592 749

约请人(Inviter)：夏铁成

主理部分：理学院数学系

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 Schrodinger (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.