Differential Inclusions in Wasserstein Spaces: The Cauchy-Lipschitz Framework

TL;DR

This paper develops a comprehensive framework for differential inclusions in Wasserstein spaces, establishing foundational theorems and applying them to mean-field optimal control problems with closed-loop controls.

Contribution

It introduces a general approach to differential inclusions in Wasserstein spaces, proving key theorems and deriving new existence results for complex control problems.

Findings

Proved Filippov's theorem in Wasserstein spaces

Established compactness of solution sets

Derived existence results for non-linear mean-field control

Abstract

In this article, we propose a general framework for the study of differential inclusions in the Wasserstein space of probability measures. Based on earlier geometric insights on the structure of continuity equations, we define solutions of differential inclusions as absolutely continuous curves whose driving velocity fields are measurable selections of multifunction taking their values in the space of vector fields. In this general setting, we prove three of the founding results of the theory of differential inclusions: Filippov's theorem, the Relaxation theorem, and the compactness of the solution sets. These contributions -- which are based on novel estimates on solutions of continuity equations -- are then applied to derive a new existence result for fully non-linear mean-field optimal control problems with closed-loop controls.