Beyond the adiabatic limit in systems with fast environments: a $\tau$-leaping algorithm

TL;DR

This paper introduces a novel $ au$-leaping algorithm that accurately captures environmental noise beyond the adiabatic limit in stochastic systems with fast-changing environments, improving efficiency and fidelity.

Contribution

The paper presents a $ au$-leaping method incorporating environmental stochasticity through Gaussian approximations, extending simulation accuracy beyond the adiabatic limit.

Findings

The algorithm performs well with fast environmental dynamics.

It requires less computational time than full system-environment simulations.

Effective for both discrete and continuous environmental states.

Abstract

We propose a $\tau$-leaping simulation algorithm for stochastic systems subject to fast environmental changes. Similar to conventional $\tau$-leaping the algorithm proceeds in discrete time steps, but as a principal addition it captures environmental noise beyond the adiabatic limit. The key idea is to treat the input rates for the $\tau$-leaping as (clipped) Gaussian random variables with first and second moments constructed from the environmental process. In this way, each step of the algorithm retains environmental stochasticity to sub-leading order in the time scale separation between system and environment. We test the algorithm on several toy examples with discrete and continuous environmental states, and find good performance in the regime of fast environmental dynamics. At the same time, the algorithm requires significantly less computing time than full simulations of the combined system and environment. In this context we also discuss several methods for the simulation of stochastic population dynamics in time-varying environments with continuous states.