Stochastic switching in slow-fast systems: a large fluctuation approach
Christoffer R. Heckman, Ira B. Schwartz

TL;DR
This paper introduces a perturbation method using a WKB ansatz to predict rare event rates in slow-fast stochastic systems, validated on a stochastic Duffing oscillator with accurate exponential scaling predictions.
Contribution
It develops a novel probabilistic approach for rare event prediction in singularly perturbed stochastic systems using a boundary value problem framework.
Findings
Accurately predicts switching times between states.
Matches numerical simulations with exponential scaling.
Reduces system complexity via slow manifold analysis.
Abstract
In this paper we develop a perturbation method to predict the rate of occurrence of rare events for singularly perturbed stochastic systems using a probability density function approach. In contrast to a stochastic normal form approach, we model rare event occurrences due to large fluctuations probabilistically and employ a WKB ansatz to approximate their rate of occurrence. This results in the generation of a two-point boundary value problem that models the interaction of the state variables and the most likely noise force required to induce a rare event. The resulting equations of motion of describing the phenomenon are shown to be singularly perturbed. Vastly different time scales among the variables are leveraged to reduce the dimension and predict the dynamics on the slow manifold in a deterministic setting. The resulting constrained equations of motion may be used to directly…
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