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

报告人：李彪教授

报告摘要：In thispaper,we propose mix-training physics-informed neural networks (PINNs). This is 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 Schrödinger (NLS) equation. Compared with the original PINNs, numerical resultsshow that this model can not only quickly recover the dynamical behavior of the rogue waves of NLS equation, but also improve its approximation ability and absolute error accuracy significantly, and the prediction accuracy has been improved by two tothree ordersof magnitude. In particular, when the space–time domain of the solution expands, or the solution has a local sharp region, the proposed model still hashighprediction accuracy.

报告人简介：李彪,宁波大学数学与统计学院教授，博导。主要从事非线性数学物理，可积系统及应用，深度学习等方面的研究。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项和参加国家自然科学基金重点项目1项。发表论文SCI论文100余篇，他引3千多次。

报告时间：2023年9月27日15:40-16:20

腾讯会议：191-773-278

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2023年9月26日