报告时间：2022年6月9日10:00-11:00


报告人： 翟佳羽 (UMASS-Amherst)

腾讯会议ID： 575-790-568

报告摘要： In the application of stochastic differential equations, one important topic is to find the evolution of the probability distribution of the solution process. This distribution is driven by a partial differential equation called Fokker-Planck equation. Since the systems are usually high dimensional, traditional numerical methods have their limitations to solve it with low cost. In this talk, a data-driven method and a machine learning method will be presented to solve it. As well, a sensitivity analysis about data-driven methods will also be shown.

报告人简介：Dr. Jiayu Zhai graduated from Louisiana State University with a PhD degree in math in 2018. After his graduation, he became a visiting assistant professor at the Department of Mathematics and Statistics in University of Massachusetts Amherst. His research includes computational methods and stochastic analysis. In computational methods, he focuses on the computation of long-time behaviors in the stochastic dynamical systems. This involves computational and data-driven methods for solving the invariant measure of the solution process, and the transitions in the system as rare events. In stochastic analysis, his interests are stochastic integration, white noise distribution theory and their applications in (particularly non-adapted) stochastic differential equations.


邀请人：陈剑宇