The blending region hybrid framework for the simulation of stochastic reaction-diffusion processes

TL;DR

This paper introduces a hybrid simulation framework for stochastic reaction-diffusion systems that combines coarse-grained and fine-grained models within a blending region, balancing accuracy and computational efficiency.

Contribution

It proposes a novel hybrid paradigm with overlapping models and blending functions to accurately simulate multiscale reaction-diffusion processes.

Findings

Successfully simulated four reaction-diffusion scenarios

Demonstrated the hybrid model's accuracy and efficiency

Validated the blending approach for multiscale systems

Abstract

The simulation of stochastic reaction-diffusion systems using fine-grained representations can become computationally prohibitive when particle numbers become large. If particle numbers are sufficiently high then it may be possible to ignore stochastic fluctuations and use a more efficient coarse-grained simulation approach. Nevertheless, for multiscale systems which exhibit significant spatial variation in concentration, a coarse-grained approach may not be appropriate throughout the simulation domain. Such scenarios suggest a hybrid paradigm in which a computationally cheap, coarse-grained model is coupled to a more expensive, but more detailed fine-grained model enabling the accurate simulation of the fine-scale dynamics at a reasonable computational cost. In this paper, in order to couple two representations of reaction-diffusion at distinct spatial scales, we allow them to overlap in a "blending region". Both modelling paradigms provide a valid representation of the particle density in this region. From one end of the blending region to the other, control of the implementation of diffusion is passed from one modelling paradigm to another through the use of complementary "blending functions" which scale up or down the contribution of each model to the overall diffusion. We establish the reliability of our novel hybrid paradigm by demonstrating its simulation on four exemplar reaction-diffusion scenarios.