The Spatial Regime Conversion Method

We present the spatial regime conversion method (SRCM), a novel hybrid modelling framework for simulating reaction–diffusion systems that adaptively combines stochastic discrete and deterministic continuum representations. Extending the regime conversion method (RCM) to spatial settings, the SRCM employs a discrete reaction–diffusion master equation (RDME) representation in regions of low concentration and continuum partial differential equations (PDEs) where concentrations are high, dynamically switching based on local thresholds. This is an advancement over the existing methods in the literature, requiring no fixed spatial interfaces, enabling efficient and accurate simulation of systems in which stochasticity plays a key role but is not required uniformly across the domain. We specify the full mathematical formulation of the SRCM, including conversion reactions, hybrid kinetic rules, and consistent numerical updates. The method is validated across several one-dimensional test systems, including simple diffusion from a region of high concentration, the formation of a morphogen gradient, and the propagation of FKPP travelling waves. The results show that the SRCM captures key stochastic features while offering substantial gains in computational efficiency over fully stochastic models.

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Journal
Mathematics
Published
2025-10-26
DOI
https://doi.org/10.3390/math13213406
Primary Topic
Ecosystem dynamics and resilience
Type
article
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article

The Spatial Regime Conversion Method

Cyril T.M. Cameron, Cameron A. Smith, Christian A. Yates
Mathematics
Ecosystem dynamics and resilience
article

The Spatial Regime Conversion Method

Cyril T.M. Cameron, Cameron A. Smith, Christian A. Yates
article en

Abstract

We present the spatial regime conversion method (SRCM), a novel hybrid modelling framework for simulating reaction–diffusion systems that adaptively combines stochastic discrete and deterministic continuum representations. Extending the regime conversion method (RCM) to spatial settings, the SRCM employs a discrete reaction–diffusion master equation (RDME) representation in regions of low concentration and continuum partial differential equations (PDEs) where concentrations are high, dynamically switching based on local thresholds. This is an advancement over the existing methods in the literature, requiring no fixed spatial interfaces, enabling efficient and accurate simulation of systems in which stochasticity plays a key role but is not required uniformly across the domain. We specify the full mathematical formulation of the SRCM, including conversion reactions, hybrid kinetic rules, and consistent numerical updates. The method is validated across several one-dimensional test systems, including simple diffusion from a region of high concentration, the formation of a morphogen gradient, and the propagation of FKPP travelling waves. The results show that the SRCM captures key stochastic features while offering substantial gains in computational efficiency over fully stochastic models.

MathematicsVol. 13(21)
University of Bath (GB)
Openalex Percentile: Top 71%
Ecosystem dynamics and resilience
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The Spatial Regime Conversion Method — Cyril T.M. Cameron, Cameron A. Smith, et al. · Mathematics (2025) | TGRS Research Map | TGRS