报告题目：Controlling mean exit time of stochastic dynamical systems based on quasipotential and machine learning

报告人： 袁胜兰（Institut für Mathematik, Universität Augsburg）

报告时间：2023年6月21日上午10:00

报告地点：明故宫校区18号楼705会议室

主办单位：航空航天结构力学及控制全国重点实验室、振动工程研究所、航空学院、校科协、国际合作处

报告内容摘要：The mean exit time escaping basin of attraction in the presence of white noise is of practical importance in various scientific fields. In this work, we propose a strategy to control mean exit time of general stochastic dynamical systems to achieve a desired value based on the quasipotential concept and machine learning. Specifically, we develop a neural network architecture to compute the global quasipotential function. Then we design a systematic iterated numerical algorithm to calculate the controller for a given mean exit time. Moreover, we identify the most probable path between metastable attractors with the help of the effective Hamilton-Jacobi scheme and the trained neural network. Numercal experiments with various dimensions and structures demonstrate that our control strategy is effective and sufficiently accurate.  

报告人简介：袁胜兰，助理研究员。2017年9月至2018年8月前往德累斯顿工业大学CSC联合培养博士。2019年6月获华中科技大学概率论与数理统计专业博士学位。随后加入华中科技大学人工智能与自动化学院从事博士后研究。而后任德国奥格斯堡大学助理研究员职位。研究方向为 Lévy过程驱动的随机动力系统、量子力学、统计物理和随机分析。近五年在 SIAM Journal on Applied Dynamical Systems、Journal of Statistical Mechanics、 Analysis and Applications等国际重要期刊上发表 14篇学术论文。