Application of importance sampling to the computation of large deviations in non-equilibrium processes

TL;DR

This paper introduces an importance sampling algorithm that efficiently estimates rare event probabilities in non-equilibrium systems by using modified dynamics, significantly outperforming direct simulation methods.

Contribution

The paper presents a novel importance sampling algorithm tailored for non-equilibrium processes, enabling accurate probability estimation of rare events.

Findings

Significant reduction in computational effort compared to direct simulation.

Successful application to models of particle and heat transport.

Large improvements in estimating rare event probabilities.

Abstract

We present an algorithm for finding the probabilities of rare events in nonequilibrium processes. The algorithm consists of evolving the system with a modified dynamics for which the required event occurs more frequently. By keeping track of the relative weight of phase-space trajectories generated by the modified and the original dynamics one can obtain the required probabilities. The algorithm is tested on two model systems of steady-state particle and heat transport where we find a huge improvement from direct simulation methods.