Stochastic representation of processes with resetting

TL;DR

This paper introduces a comprehensive stochastic framework using jump-diffusion models to analyze processes with resetting, providing analytical tools and extending results to non-homogeneous cases, applicable to a wide range of stochastic systems.

Contribution

It presents a novel stochastic representation for processes with resetting, enabling analytical and simulation-based analysis, and extends fundamental properties to non-homogeneous resetting scenarios.

Findings

Derived fundamental properties of Brownian motion with Poissonian resetting.

Established explicit probability density functions and moments.

Extended results to time-nonhomogeneous resetting cases.

Abstract

In this paper we introduce a general stochastic representation for an important class of processes with resetting. It allows to describe any stochastic process intermittently terminated and restarted from a predefined random or non-random point. Our approach is based on stochastic differential equations called jump-diffusion models. It allows to analyze processes with resetting both, analytically and using Monte Carlo simulation methods. To depict the strength of our approach, we derive a number of fundamental properties of Brownian motion with Poissonian resetting, such as: the It\^o lemma, the moment-generating function, the characteristic function, the explicit form of the probability density function, moments of all orders, various forms of the Fokker-Planck equation, infinitesimal generator of the process and its adjoint operator. Additionally, we extend the above results to the case of time-nonhomogeneous Poissonian resetting. This way we build a general framework for the analysis of any stochastic process with intermittent random resetting.