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

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.

5 pages, 4 figures; some modifications

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• Finite-Time and -Size Scalings in the Evaluation of Large Deviation Functions: Numerical Approach in Continuous Time
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• Equilibrium free energy differences from a linear nonequilibrium equality
• Efficient large deviation estimation based on importance sampling
• Adaptive power method for estimating large deviations in Markov chains
• Convergence of the Integral Fluctuation Theorem estimator for nonequilibrium Markov systems
• Yielding and large deviations in micellar gels: a model
• Self-interacting processes via Doob conditioning