Convergence analysis for minimum action methods coupled with a finite difference method

TL;DR

This paper analyzes the convergence properties of minimum action methods combined with finite difference schemes for stochastic differential equations, providing theoretical convergence rates for different noise types.

Contribution

It presents the first convergence analysis of discrete Freidlin--Wentzell action functionals coupled with finite difference methods, including rates for multiplicative and additive noise cases.

Findings

Convergence order of 1/2 for multiplicative noise

Convergence order of 1 for additive noise

Stochastic θ-method converges in large deviations sense

Abstract

The minimum action method (MAM) is an effective approach to numerically solving minimums and minimizers of Freidlin--Wentzell (F-W) action functionals, which is used to study the most probable transition path and probability of the occurrence of transitions for stochastic differential equations (SDEs) with small noise. In this paper, we focus on MAMs based on a finite difference method, and present the convergence analysis of minimums and minimizers of the discrete F-W action functional. The main result shows that the convergence orders of the minimum of the discrete F-W action functional in the cases of multiplicative noises and additive noises are $1/2$ and $1$, respectively. Our main result also reveals the convergence of the stochastic $\theta$-method for SDEs with small noise in terms of large deviations.