Most probable transition paths in piecewise-smooth stochastic differential equations

TL;DR

This paper extends large deviation theory to piecewise-smooth stochastic differential equations, deriving a rate functional that accounts for sliding paths and applying it to case studies revealing complex transition phenomena.

Contribution

It develops a path integral framework for piecewise-smooth SDEs, incorporating sliding paths into the Freidlin-Wentzell theory and deriving a new rate functional.

Findings

Derived a rate functional including sliding paths

Identified non-uniqueness of most probable paths

Observed noise-induced sliding phenomena

Abstract

We develop a path integral framework for determining most probable paths in a class of systems of stochastic differential equations with piecewise-smooth drift and additive noise. This approach extends the Freidlin-Wentzell theory of large deviations to cases where the system is piecewise-smooth and may be non-autonomous. In particular, we consider an $n-$dimensional system with a switching manifold in the drift that forms an $(n-1)-$dimensional hyperplane and investigate noise-induced transitions between metastable states on either side of the switching manifold. To do this, we mollify the drift and use $\Gamma-$convergence to derive an appropriate rate functional for the system in the piecewise-smooth limit. The resulting functional consists of the standard Freidlin-Wentzell rate functional, with an additional contribution due to times when the most probable path slides in a crossing region of the switching manifold. We explore implications of the derived functional through two case studies, which exhibit notable phenomena such as non-unique most probable paths and noise-induced sliding in a crossing region.