題目一：Efficient synchronous retrieval of OAM modes and AT strength using multi-task neural networks

内容簡介：When transmitted through the atmospheric channel, OAM beams are influenced by the random fluctuations in the refractive index caused by atmospheric turbulence, resulting in phase distortion and intensity dispersion of the beams, leading to severe signal interference. Due to the high randomness of atmospheric turbulence, it is essential for OAM mode recognition methods to have good stability to ensure communication quality. We establish an equivalence relationship between the continuous dynamics system and the network unit RUEM, ensuring its stability through theoretical derivation and numerical experiments. We propose a multitask neural network model, OATNN, embedded with RUEM to achieve efficient simultaneous recognition of turbulence intensity in atmospheric turbulence environments and OAM modes in free-space optical communication systems.

報告人：尹偉石

報告人簡介：長春理工大學數學與統計學院副教授，碩士研究生導師。主要研究興趣是數學物理反問題、機器學習算法的設計與理論分析和微分方程數值解等。在JCP，JCAM，CICP，IPI等期刊發表論文20餘篇，主持并參與國家自然科學基金、吉林省科技廳基金和吉林省教育廳基金6項。目前擔任中國仿真學會不确定系統分析與仿真專委會委員、Math Review評論員以及IPIA會員。

題目二：An online interactive physical information adversarial network for solving mean-field games

内容簡介：In this talk, We propose an online interactive physical information adversarial network (IPIAN) to solve mean-field games (MFGs) from the perspective of physical information interaction. We consider the variational dual structure of MFGs, treat the interactions between agents as physical information interactions, simulate the evolution of individual strategy choices and the overall distribution of agents, and then use adversarial networks to solve MFGs.Based on the generation of an adversarial framework, we use two online physical information networks to solve the value function and the density function and train the networks to approximate the solution of MFGs by adversarial means. A self-attention mechanism is introduced to focus on strategic physical information to improve the expressiveness and accuracy of IPIAN. Numerical experiments demonstrate the effectiveness of IPIAN in solving high-dimensional mean-field game models by performing obstacle avoidance experiments on quadrotors in different contexts.

報告人：孟品超

報告人簡介：長春理工大學數學與統計學院教授，碩士研究生導師。主要研究興趣是數學物理反問題、機器學習算法的設計與理論分析等。在JCAM，OE，CICP，IPI等期刊發表論文20餘篇，主持并參與國家自然科學基金、軍委科技委領域基金、吉林省科技廳基金、吉林省教育廳基金8項。目前擔任中國仿真學會不确定系統分析與仿真專委會委員、IPIA會員、吉林省運籌學會理事。

時  間：2024年8月9日（周五）下午16：00開始

地  點：南海樓330室

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2024年8月8日