Weak-noise limit of a piecewise-smooth stochastic differential equation

TL;DR

This paper examines the effectiveness of weak-noise approximations for piecewise-smooth stochastic differential equations, demonstrating their accuracy and exploring regularization techniques to handle discontinuities and singularities.

Contribution

It shows that weak-noise path integral methods accurately approximate propagators for non-smooth SDEs and discusses regularization approaches for discontinuous drifts.

Findings

Weak-noise approximation reproduces the propagator at lowest order.

Deterministic paths describe the low-noise behavior of non-smooth SDEs.

Regularization can mitigate issues with discontinuous drifts.

Abstract

We investigate the validity and accuracy of weak-noise (saddle-point or instanton) approximations for piecewise-smooth stochastic differential equations (SDEs), taking as an illustrative example a piecewise-constant SDE, which serves as a simple model of Brownian motion with solid friction. For this model, we show that the weak-noise approximation of the path integral correctly reproduces the known propagator of the SDE at lowest order in the noise power, as well as the main features of the exact propagator with higher-order corrections, provided that the singularity of the path integral associated with the non-smooth SDE is treated with some heuristics. We also show that, as in the case of smooth SDEs, the deterministic paths of the noiseless system correctly describe the behaviour of the non-smooth SDE in the low-noise limit. Finally, we consider a smooth regularisation of the piecewise-constant SDE and study to which extent this regularisation can rectify some of the problems encountered when dealing with discontinuous drifts and singularities in SDEs.