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

报告人: 袁胜兰，大湾区大学（筹）

时间: 9月20日16：00

地点: 东区管理科研楼1418

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.