Differential Inclusions in Wasserstein Spaces: The Cauchy-Lipschitz Framework

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.

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Cited by in corpus (9)

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• Dissipative probability vector fields and generation of evolution semigroups in Wasserstein spaces
• Kernel Stein Discrepancy Descent
• A Measure Theoretical Approach to the Mean-field Maximum Principle for Training NeurODEs
• Vanishing viscosity in mean-field optimal control
• Carathéodory Theory and A Priori Estimates for Continuity Inclusions in the Space of Probability Measures
• A Pontryagin Maximum Principle for agent-based models with convex state space
• Optimal transportation of measures with a parameter