Pathwise error bounds in Multiscale variable splitting methods for spatial stochastic kinetics

TL;DR

This paper develops theoretical tools to analyze the convergence and error bounds of multiscale variable splitting methods for spatial stochastic kinetics, enabling efficient approximations of complex reaction-diffusion systems without assuming bounded solutions.

Contribution

It introduces novel pathwise error bounds and conditions for multiscale convergence in spatial stochastic models, without requiring a priori boundedness of solutions.

Findings

Established well-posedness of approximations.

Quantified multiscale and splitting errors.

Validated results with computational experiments.

Abstract

Stochastic computational models in the form of pure jump processes occur frequently in the description of chemical reactive processes, of ion channel dynamics, and of the spread of infections in populations. For spatially extended models, the computational complexity can be rather high such that approximate multiscale models are attractive alternatives. Within this framework some variables are described stochastically, while others are approximated with a macroscopic point value. We devise theoretical tools for analyzing the pathwise multiscale convergence of this type of variable splitting methods, aiming specifically at spatially extended models. Notably, the conditions we develop guarantee well-posedness of the approximations without requiring explicit assumptions of \textit{a priori} bounded solutions. We are also able to quantify the effect of the different sources of errors, namely the \emph{multiscale error} and the \emph{splitting error}, respectively, by developing suitable error bounds. Computational experiments on selected problems serve to illustrate our findings.