报告人：陈勇 教授 华东师范大学

报告题目：可积深度学习的进展-PE-RWP: A deep learning framework for polynomial feature extraction of rogue wave patterns and peregrine waves localization

时间：2025年11月18日15:00-16:00

地点：数学楼2-1会议室

摘要：

In this report, we propose a deep learning-based “Polynomial Extractor for Rogue Wave Patterns (PE-RWP)”. This approach replaces traditional asymptotic analysis of high-order RW solutions under large parameter conditions, enabling automatic and precise identification of polynomial characteristics within RW patterns. The unique dual-branch network architecture (with a regression branch for identifying corresponding polynomial types) enables PE-RWP to effectively output this generalized polynomial hierarchy and recognize RW patterns subjected to arbitrary scaling and rotational transformations. Furthermore, as an application based on the mathematical theory of RW patterns, we leverage the Y-V polynomials output by PE-RWP to achieve unsupervised localization of Peregrine waves through deep learning methods. Finally, through extensive experimental evaluation, both problems polynomial extraction and Peregrine wave localization are effectively solved with high accuracy.

报告人简介：

   陈勇，华东师范大学数学学院教授，博士生导师，计算机理论所所长。长期从事非线性数学物理、可积系统、计算机代数及程序开发、可积深度学习算法、混沌理论、大气和海洋动力学等领域的研究工作，已在SCI收录的国际学术期刊上发表SCI论文300余篇，主持和参加国家自然科学基金重点项目、973项目、国家自然科学基金长江团队项目、国家自然科学基金面上项目。

邀请人：刘小川 教授