报告一：
【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】2022年12月7日下午2：00
【腾讯会议号】533 194 875
【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.
报告二：
【报告题目】可积与不可积非线性薛定谔方程
【报告人】楼森岳教授，宁波大学
【报告时间】2022年12月7日下午3：00
【腾讯会议号】533 194 875
【报告题目】本报告首先指出非线性薛定谔方程的普适性。然后罗列各种目前学术界常见的可积和不可积，局域和非局域，厄密和非厄密的非线性薛定谔方程。最后介绍目前比较关心的非线性方程的解析解类型，特别介绍一个高阶非线性薛定谔方程的孤子分子解。
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