发布时间：2025-10-14
An Optimal Control Method to Compute the Most Likely Transition Path for Stochastic Dynamical Systems with Jumps
主讲人：陈小丽
摘要：Many complex real world phenomena exhibit abrupt, intermittent or jumping behaviors, which are more suitable to be described by stochastic differential equations under non-Gaussian L\'evy noise. Among these complex phenomena, the most likely transition paths between metastable states are important since these rare events may have high impact in certain scenarios. Based on the large deviation principle, the most likely transition path could be treated as the minimizer of the rate function upon paths that connect two points. One of the challenges to calculate the most likely transition path for stochastic dynamical systems under non-Gaussian L\'evy noise is that the associated rate function can not be explicitly expressed by paths. For this reason, we formulate an optimal control problem to obtain the optimal state as the most likely transition path. We then develop a neural network method to solve this issue. Several experiments are investigated for both Gaussian and non-Gaussian cases.
主讲人简介：陈小丽，中国地质大学（武汉）特任教授。2020年博士毕业于华中科技大学。2018年9月至2020年8月在美国布朗大学进行联合培养。2021年3月至2024年3月在新加坡国立大学从事博士后研究。2024年入选湖北省高层次人才计划，主要从事随机动力系统, 机器学习与动力系统相关研究研究。已在SIAM Journal on Scientific Computing, Nature Computational Science, CMAME, Physica D等期刊发表多篇学术论文。
邀请人：李东方
时间： 2025年10月16日16:00-18:00
地点： 科技楼南楼813室