From the backward Kolmogorov PDE on the Wasserstein space to propagation of chaos for Mckean-Vlasov SDEs

TL;DR

This paper provides explicit quantitative estimates for the propagation of chaos in McKean-Vlasov SDEs, connecting PDE analysis on Wasserstein space with mean-field particle system approximations.

Contribution

It establishes new explicit error bounds for particle approximations and density differences using PDE techniques on Wasserstein space, extending previous work.

Findings

Explicit trajectory-level error estimates

First order density difference expansion

Well-posedness of backward Kolmogorov PDE on Wasserstein space

Abstract

This article is a continuation of our first work \cite{chaudruraynal:frikha}. We here establish some new quantitative estimates for propagation of chaos of non-linear stochastic differential equations in the sense of McKean-Vlasov. We obtain explicit error estimates, at the level of the trajectories, at the level of the semi-group and at the level of the densities, for the mean-field approximation by systems of interacting particles under mild regularity assumptions on the coefficients. A first order expansion for the difference between the densities of one particle and its mean-field limit is also established. Our analysis relies on the well-posedness of classical solutions to the backward Kolmogorov partial differential equations defined on the strip $[0,T] \times \mathbb{R}^d \times \mathcal{P}_2(\mathbb{R}^d)$, $\mathcal{P}_2(\mathbb{R}^d)$ being the Wasserstein space, that is, the space of probability measures on $\mathbb{R}^d$ with a finite second-order moment and also on the existence and uniqueness of a fundamental solution for the related parabolic linear operator here stated on $[0,T]\times \mathcal{P}_2(\mathbb{R}^d)$.