Distribution-Dependent SDEs for Landau Type Equations

Abstract

The distribution-dependent stochastic differential equations (DDSDEs) describe stochastic systems whose evolution is determined by both the microcosmic site and the macrocosmic distribution of the particle. The density function associated with a DDSDE solves a non-linear PDE. Due to the distribution-dependence, some standard techniques developed for SDEs do not apply. By iterating in distributions, a strong solution is constructed using SDEs with control. By proving the uniqueness, the distribution of solutions is identified with a non-linear semigroup $P_t^{\ast }$ on the space of probability measures. The exponential contraction as well as Harnack inequalities and applications are investigated for the non-linear semigroup $P_t^{\ast }$ using coupling by change of measures. The main results are illustrated by homogeneous Landau equations.

31 pages

References in corpus (1)

• Harnack Inequalities for Functional SDEs with Multiplicative Noise and Applications

Cited by in corpus (2)

• Least squares estimation for path-distribution dependent stochastic differential equations
• Distribution-Dependent Stochastic Functional Differential Equations