English

Kinetic Fokker-Planck Equations with Nonlinear Diffusion

Analysis of PDEs 2026-07-01 v1 Probability

Abstract

We study existence, regularity, and uniqueness for the nonlinear kinetic Fokker--Planck equation tf=ΔvΨ(f)vxf,ft=0=f0, \partial_t f=\Delta_v\Psi(f)-v\cdot\nabla_x f, \qquad f|_{t=0}=f_0, on R2d\mathbb R^{2d}. In the model case Ψ(r)=rs\Psi(r)=r^s, this equation couples nonlinear fast-diffusion/porous-medium type diffusion with kinetic transport. A distinctive feature is that the diffusion acts only in the velocity variable vv, so that compactness in the spatial variable xx cannot be obtained from standard elliptic estimates and must instead be recovered through the hypoelliptic structure. Under general structural assumptions on Ψ\Psi, including the fast-diffusion powers Ψ(r)=rs\Psi(r)=r^s with s(0,1)s\in(0,1), we construct nonnegative weak solutions and prove quantitative anisotropic Besov regularity estimates. Under an additional mass-critical growth condition on the fast-diffusion side, the constructed weak solution preserves mass, admits a renormalized kinetic formulation, and is unique in the L1L^1-class of mass-preserving renormalized kinetic solutions. In the power-law case Ψ(r)=rs\Psi(r)=r^s, this condition is precisely s11/ds\ge 1-1/d when d2d\ge2, while in dimension d=1d=1 the whole fast-diffusion range s(0,1)s\in(0,1) is covered. The main analytic ingredient is a parameter-dependent smoothing estimate for the kinetic semigroup generated by Ψ(ζ)Δvvx, \Psi'(\zeta)\Delta_v - v\cdot\nabla_x , which quantitatively tracks the dependence on the kinetic level ζ\zeta. Combined with the kinetic formulation, this estimate yields compactness in both spatial and velocity variables for the nonlinear hypoelliptic problem. As an application, we also obtain martingale-problem solutions to the associated distributional-density dependent stochastic differential equation.

Cite

@article{arxiv.2607.00458,
  title  = {Kinetic Fokker-Planck Equations with Nonlinear Diffusion},
  author = {Zimo Hao and Zhengyan Wu and Xicheng Zhang},
  journal= {arXiv preprint arXiv:2607.00458},
  year   = {2026}
}