# Quantitative Propagation of Chaos in the bimolecular chemical reaction-diffusion model

## TL;DR

This paper proves that a stochastic particle system modeling bimolecular chemical reactions converges to a mean field PDE in the large particle limit, providing a quantitative propagation of chaos result using a novel probabilistic approach.

## Contribution

The paper introduces a new probabilistic proof for quantitative propagation of chaos in a reaction-diffusion particle system, extending previous entropy-based methods.

## Key findings

- Convergence of particle system to reaction-diffusion PDE as particle number grows
- Quantitative estimates on the rate of propagation of chaos
- Development of a new martingale-based proof technique

## Abstract

We study a stochastic system of $N$ interacting particles which models bimolecular chemical reaction-diffusion. In this model, each particle $i$ carries two attributes: the spatial location $X_t^i\in \mathbb{T}^d$, and the type $\Xi_t^i\in \{1,\cdots,n\}$. While $X_t^i$ is a standard (independent) diffusion process, the evolution of the type $\Xi_t^i$ is described by pairwise interactions between different particles under a series of chemical reactions described by a chemical reaction network. We prove that in the large particle limit the stochastic dynamics converges to a mean field limit which is described by a nonlocal reaction-diffusion partial differential equation. In particular, we obtain a quantitative propagation of chaos result for the interacting particle system. Our proof is based on the relative entropy method used recently by Jabin and Wang \cite{JW18}. The key ingredient of the relative entropy method is a large deviation estimate for a special partition function, which was proved previously by technical combinatorial estimates. We give a simple probabilistic proof based on a novel martingale argument.