Pathwise error bounds in Multiscale variable splitting methods for spatial stochastic kinetics
This work provides rigorous error analysis for approximate multiscale models in spatial stochastic kinetics, benefiting researchers in computational chemistry and biology who need efficient simulations.
The paper develops theoretical tools for analyzing pathwise multiscale convergence of variable splitting methods in spatial stochastic kinetics, providing error bounds for multiscale and splitting errors without requiring a priori bounded solutions.
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.