English

Nonlinear Fokker-Planck equations for Probability Measures on Path Space and Path-Distribution Dependent SDEs

Probability 2020-08-20 v2

Abstract

By investigating path-distribution dependent stochastic differential equations, the following type of nonlinear Fokker--Planck equations for probability measures (μt)t0(\mu_t)_{t \geq 0} on the path space C:=C([r0,0];Rd),\mathcal C:=C([-r_0,0];\mathbb R^d), is analyzed: tμ(t)=Lt,μtμt,  t0,\partial_t \mu(t)=L_{t,\mu_t}^*\mu_t,\ \ t\ge 0, where μ(t)\mu(t) is the image of μt\mu_t under the projection Cξξ(0)Rd\mathcal C\ni\xi\mapsto \xi(0)\in\mathbb R^d, and Lt,μ(ξ):=12i,j=1daij(t,ξ,μ)2ξ(0)iξ(0)j+i=1dbi(t,ξ,μ)ξ(0)i,  t0,ξC,μPC.L_{t,\mu}(\xi):= \frac 1 2\sum_{i,j=1}^d a_{ij}(t,\xi,\mu)\frac{\partial^2} {\partial_{\xi(0)_i} \partial_{\xi(0)_j}} +\sum_{i=1}^d b_i(t,\xi,\mu)\frac{\partial}{\partial_{\xi(0)_i}},\ \ t\ge 0, \xi\in \mathcal C, \mu\in \mathcal P^{\mathcal C}. Under reasonable conditions on the coefficients aija_{ij} and bib_i, the existence, uniqueness, Lipschitz continuity in Wasserstein distance, total variational norm and entropy, as well as derivative estimates are derived for the martingale solutions.

Keywords

Cite

@article{arxiv.1709.00556,
  title  = {Nonlinear Fokker-Planck equations for Probability Measures on Path Space and Path-Distribution Dependent SDEs},
  author = {Xing Huang and Michael Röckner and Feng-Yu Wang},
  journal= {arXiv preprint arXiv:1709.00556},
  year   = {2020}
}

Comments

22 pages

R2 v1 2026-06-22T21:31:15.202Z