报告人：任维清 新加坡国立大学

报告时间：2020-11-30上午9:30-10:30      

报告地址：腾讯会议ID：399 975 040

摘要: The quasipotential is a generalization of the concept of potentials to non-equilibrium systems. In the analysis of rare events in stochastic dynamics, it plays a central role in characterizing the statistics of transition events and the maximum likelihood transition paths. The quasipotential has a simple mathematical description and satisfies a Hamilton-Jacobi equation. However, computing the quasipotential is challenging, especially in high dimensional dynamical systems. In this talk, we present an efficient machine learning method to resolve this problem. We demonstrate on various dynamical systems that our method can effectively compute accurate and global quasipotential landscapes. This makes it a promising method to enable the general application of quasipotential analysis to non-equilibrium systems. This is a joint work with Qianxiao Li and Bo Lin (NUS).      

报告人简介：任维清，新加坡国立大学教授。2002年毕业于纽约大学Courant研究所并获博士学位。先后在普林斯顿高等研究院和普林斯顿大学从事博士后研究工作，2005年至2011年在纽约大学Courant研究所担任助理教授。任教授主要从事科学计算的研究, 在稀有事件的理论及计算方法，多尺度算法和分析，以及流体中的滑移接触线问题等领域做出重要贡献。任教授曾获Alfred P. Sloan Research Fellowship (2007) 和冯康科学计算奖 (2015)。

邀请人：张亚楠