Approximate optimal controls via instanton expansion for low temperature free energy computation
Gr\'egoire Ferr\'e, Tobias Grafke

TL;DR
This paper introduces a novel method for approximating optimal controls in low temperature regimes to efficiently compute free energies, reducing variance in Monte Carlo simulations through instanton expansion techniques.
Contribution
It develops a new strategy to construct approximate optimal controls using instanton expansion, improving free energy estimation in low temperature diffusion processes.
Findings
Reduces variance of Monte Carlo estimators in low temperature regimes.
Provides a perturbative formula for free energy in small temperature limit.
Explicitly computes low-order expansions and discusses extensions.
Abstract
The computation of free energies is a common issue in statistical physics. A natural technique to compute such high dimensional integrals is to resort to Monte Carlo simulations. However these techniques generally suffer from a high variance in the low temperature regime, because the expectation is often dominated by high values corresponding to rare system trajectories. A standard way to reduce the variance of the estimator is to modify the drift of the dynamics with a control enhancing the probability of rare event, leading to so-called importance sampling estimators. In theory, the optimal control leads to a zero-variance estimator; it is however defined implicitly and computing it is of the same difficulty as the original problem. We propose here a general strategy to build approximate optimal controls in the small temperature limit for diffusion processes, with the first goal to…
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