Multiscale Gentlest Ascent Dynamics for Saddle Point in Effective Dynamics of Slow-Fast System

TL;DR

This paper introduces a multiscale numerical method based on gentlest ascent dynamics to efficiently locate saddle points in effective dynamics of slow-fast stochastic systems, relevant for transition state analysis in chemical physics.

Contribution

It develops a multiscale approach combining gentlest ascent dynamics with acceleration techniques to find saddle points in complex stochastic systems.

Findings

Successfully applied to stochastic ODEs and PDEs

Achieves efficient convergence to saddle points

Demonstrates acceleration techniques improve performance

Abstract

Here we present a multiscale method to calculate the saddle point associated with the effective dynamics arising from a stochastic system which couples slow deterministic drift and fast stochastic dynamics. This problem is motivated by the transition states on free energy surfaces in chemical physics. Our method is based on the gentlest ascent dynamics which couples the position variable and the direction variable and has the local convergence to saddle points. The dynamics of the direction vector is derived in terms of the covariance function with respective to the equilibrium distribution of the fast stochastic process. We apply the multiscale numerical methods to efficiently solve the obtained multiscale gentlest ascent dynamics, {and discuss the acceleration techniques based on the adaptive idea.} The examples of stochastic ordinary and partial differential equations are presented.