Proximal Recursion for Solving the Fokker-Planck Equation

TL;DR

This paper introduces a novel meshless numerical method based on proximal recursion and entropic regularization to efficiently solve the Fokker-Planck equation for nonlinear stochastic systems, avoiding traditional discretization issues.

Contribution

The authors develop a new meshless approach using proximal recursion and entropic regularization, providing a fast, convergent solution to the Fokker-Planck equation without spatial discretization.

Findings

Enables fast, meshless computation of the Fokker-Planck equation.

Proves convergence due to contraction properties.

Applicable to high-dimensional stochastic systems.

Abstract

We develop a new method to solve the Fokker-Planck or Kolmogorov's forward equation that governs the time evolution of the joint probability density function of a continuous-time stochastic nonlinear system. Numerical solution of this equation is fundamental for propagating the effect of initial condition, parametric and forcing uncertainties through a nonlinear dynamical system, and has applications encompassing but not limited to forecasting, risk assessment, nonlinear filtering and stochastic control. Our methodology breaks away from the traditional approach of spatial discretization for solving this second-order partial differential equation (PDE), which in general, suffers from the "curse-of-dimensionality". Instead, we numerically solve an infinite dimensional proximal recursion in the space of probability density functions, which is theoretically equivalent to solving the Fokker-Planck-Kolmogorov PDE. We show that the dual formulation along with the introduction of an entropic regularization, leads to a smooth convex optimization problem that can be implemented via suitable block co-ordinate iteration and has fast convergence due to certain contraction property that we establish. This approach enables meshless implementation leading to remarkably fast computation.