Efficient Rare Event Simulation by Optimal Nonequilibrium Forcing

Abstract

Rare event simulation and estimation for systems in equilibrium are among the most challenging topics in molecular dynamics. As was shown by Jarzynski and others, nonequilibrium forcing can theoretically be used to obtain equilibrium rare event statistics. The advantage seems to be that the external force can speed up the sampling of the rare events by biasing the equilibrium distribution towards a distribution under which the rare events is no longer rare. Yet algorithmic methods based on Jarzynski's and related results often fail to be efficient because they are based on sampling in path space. We present a new method that replaces the path sampling problem by minimization of a cross-entropy-like functional which boils down to finding the optimal nonequilibrium forcing. We show how to solve the related optimization problem in an efficient way by using an iterative strategy based on milestoning.

15 pages, 7 figures

References in corpus (3)

• Generalized Jarzynski Equality under Nonequilibrium Feedback Control
• Nonequilibrium Detailed Fluctuation Theorem for Repeated Discrete Feedback
• Optimal estimators and asymptotic variances for nonequilibrium path-ensemble averages

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