Linearization of Nonlinear Fokker-Planck Equations and Applications
Panpan Ren, Michael Rockner, Feng-Yu Wang

TL;DR
This paper develops a framework linking nonlinear Fokker-Planck equations to linearized versions on measure spaces, enabling probabilistic representations and analysis of complex stochastic PDEs with applications to McKean-Vlasov SDEs and Schrödinger equations.
Contribution
It introduces a novel linearization approach for nonlinear Fokker-Planck equations on measure spaces, connecting them to diffusion processes and McKean-Vlasov SDEs under minimal regularity assumptions.
Findings
Explicit linearization of nonlinear Fokker-Planck equations.
Characterization of ergodicity via asymptotic properties.
Probabilistic representation of Schrödinger PDE solutions.
Abstract
We associate a coupled nonlinear Fokker-Planck equation on , i.e. with solution paths in , to a linear Fokker-Planck equation for probability measures on the product space , i.e. with solution paths in . We explicitly determine the corresponding linear Kolmogorov operator using the natural tangent bundle over with corresponding gradient operator . Then it is proved that the diffusion process generated by on is intrinsically related to the solution of a McKean-Vlasov stochastic differential equation (SDE). We also characterize the ergodicity of the diffusion process generated by in terms of asymptotic properties of the coupled nonlinear Fokker-Planck equation. Another main result of the paper is that the restricted…
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Taxonomy
TopicsStochastic processes and financial applications · Statistical Mechanics and Entropy · Mathematical Biology Tumor Growth
