Convergence of Stochastic Interacting Particle Systems in Probability under a Sobolev Norm

TL;DR

This paper proves that regularized empirical measures of interacting particle systems with Brownian motion converge in probability to mean-field PDE solutions under Sobolev norms, covering various interaction types including Coulomb and Newton forces.

Contribution

It establishes convergence results for particle systems with bounded, Lipschitz, and certain regularity conditions on interactions, extending to non-regularized cases for key systems.

Findings

Convergence in probability under Sobolev norms for systems with bounded, Lipschitz interactions.

Global convergence for Coulomb interactions over any time interval.

Convergence within the maximal existence time for Newton force interactions.

Abstract

In this paper, we consider particle systems with interaction and Brownian motion. We prove that when the initial data is from the sampling of Chorin's method, i.e., the initial vertices are on lattice points $hi\in \mathbb{R}^d$ with mass $\rho_0(hi) h^d$, where $\rho_0$ is some initial density function, then the regularized empirical measure of the interacting particle system converges in probability to the corresponding mean-field partial differential equation with initial density $\rho_0$, under the Sobolev norm of $L^\infty(L^2)\cap L^2(H^1)$. Our result is true for all those systems when the interacting function is bounded, Lipschitz continuous and satisfies certain regular condition. And if we further regularize the interacting particle system, it also holds for some of the most important systems of which the interacting functions are not. For systems with repulsive Coulomb interaction, this convergence holds globally on any interval $[0,t]$. And for systems with attractive Newton force as interacting function, we have convergence within the largest existence time of the regular solution of the corresponding Keller-Segel equation.