A trajectorial approach to the gradient flow of McKean-Vlasov SDEs with mobility

TL;DR

This paper develops a trajectorial approach to the gradient flow of McKean-Vlasov SDEs with mobility, linking stochastic analysis with Wasserstein quasi-metrics and applying it to physical models like the Fermi-Dirac-Fokker-Planck equation.

Contribution

It introduces a novel trajectorial representation of the gradient flow for McKean-Vlasov SDEs using a modified Wasserstein metric, extending the understanding of energy dissipation in these systems.

Findings

Derived the trajectorial relative entropy dissipation identity.

Demonstrated energy dissipation in the Fermi-Dirac-Fokker-Planck equation.

Raised questions on condensation phenomena and convergence rates.

Abstract

We establish the gradient flow representation of diffusion with mobility $b$ with respect to the modified Wasserstein quasi-metric $W_h$, where $h(r)=rb(r)$. The appropriate selection of the free energy functional depends on the specific form of the generalized entropy. Different from the JKO scheme, we derive the trajectorial version of the relative entropy dissipation identity for the McKean-Vlasov stochastic differential equation (SDE) with Nemytskii-type coefficients, utilizing techniques from stochastic analysis. Based on this, we demonstrate that the trajectorial average of the solution process to the McKean-Vlasov SDE, with respect to the underlying measure, corresponds to the rate of dissipation of the free energy. As an application, we present the energy dissipation of the Fermi-Dirac-Fokker-Planck equation, a model widely used in physics and biology to describe saturation effects. Inspired by numerical simulations, we propose two questions on condensation phenomena and non-exponential convergence rate.