## Abstract

Reaction-diffusion models are used to describe systems in fields as diverse as physics, chemistry, ecology and biology. The fundamental quantities in such models are individual entities such as atoms and molecules, bacteria, cells or animals, which move and/or react in a stochastic manner. If the number of entities is large, accounting for each individual is inefficient, and often partial differential equation (PDE) models are used in which the stochastic behaviour of individuals is replaced by a description of the averaged, or mean behaviour of the system. In some situations the number of individuals is large in certain regions and small in others. In such cases, a stochastic model may be inefficient in one region, and a PDE model inaccurate in another. To overcome this problem, we develop a scheme which couples a stochastic reaction-diffusion system in one part of the domain with its mean field analogue, i.e. a discretised PDE model, in the other part of the domain. The interface in between the two domains occupies exactly one lattice site and is chosen such that the mean field description is still accurate there. In this way errors due to the flux between the domains are small. Our scheme can account for multiple dynamic interfaces separating multiple stochastic and deterministic domains, and the coupling between the domains conserves the total number of particles. The method preserves stochastic features such as extinction not observable in the mean field description, and is significantly faster to simulate on a computer than the pure stochastic model.

Original language | English |
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Pages (from-to) | 429-445 |

Number of pages | 17 |

Journal | Journal of Computational Physics |

Volume | 299 |

DOIs | |

Publication status | Published - 5 Oct 2015 |

## Keywords

- Fisher-Kolmogorov equation
- Hybrid model
- Lotka-Volterra equation
- Reaction-diffusion system
- Stochastic model
- Monte Carlo (MC) simulation
- Optimization

## ASJC Scopus subject areas

- Physics and Astronomy (miscellaneous)
- Computer Science Applications
- Applied Mathematics
- Modelling and Simulation