报告题目：Efficient minimum action method for capturing the most probable transition path

报告人： Xiaoliang Wan (Louisiana State University)

报告时间： 2017年6月26号 16:00-17:00

报告地点： 恩明楼205

Abstract ： Minimum action method (MAM) plays an important role in minimizing the Freidlin-Wentzell action functional, which is the central object of the Freidlin-Wentzell theory of large deviations for small-noise-induced transitions in stochastic dynamical systems. Because of the demanding computation cost, especially in spatially extended systems, numerical efficiency is a critical issue for MAM. Difficulties come from both temporal and spatial discretizations. One severe hurdle for the application of MAM to large scale systems is the global reparametrization in time direction, which is needed in most versions of MAM to achieve accuracy. We have developed a new version of MAM based on optimal linear time scaling where the global reparametrization is replaced by hp adaptivity guided by a posteriori error estimates. More specifically, we use the zero-Hamiltonian constraint to define an indicator to measure the error induced by linear time scaling, and the derivative recovery technique to construct an error indicator and a regularity indicator for the transition paths approximated by finite elements.