Filtering Dynamical Systems Using Observations of Statistics

TL;DR

This paper introduces the ensemble Fokker-Planck filter (EnFPF), a novel ensemble method for filtering dynamical systems using statistical observations, capable of correcting ensemble statistics and accelerating convergence.

Contribution

The paper develops the EnFPF, an ensemble-based filtering algorithm for densities, approximating the Kalman-Bucy filter under certain assumptions and demonstrating practical advantages in complex systems.

Findings

EnFPF approximates the Kalman-Bucy filter in restrictive cases.

EnFPF corrects ensemble statistics effectively.

EnFPF accelerates convergence to invariant densities.

Abstract

We consider the problem of filtering dynamical systems, possibly stochastic, using observations of statistics. Thus, the computational task is to estimate a time-evolving density $\rho(v, t)$ given noisy observations of the true density $\rho^\dagger$; this contrasts with the standard filtering problem based on observations of the state $v$. The task is naturally formulated as an infinite-dimensional filtering problem in the space of densities $\rho$. However, for the purposes of tractability, we seek algorithms in state space; specifically, we introduce a mean-field state-space model, and using interacting particle system approximations to this model, we propose an ensemble method. We refer to the resulting methodology as the ensemble Fokker-Planck filter (EnFPF). Under certain restrictive assumptions, we show that the EnFPF approximates the Kalman-Bucy filter for the Fokker-Planck equation, which is the exact solution to the infinite-dimensional filtering problem. Furthermore, our numerical experiments show that the methodology is useful beyond this restrictive setting. Specifically, the experiments show that the EnFPF is able to correct ensemble statistics, to accelerate convergence to the invariant density for autonomous systems, and to accelerate convergence to time-dependent invariant densities for non-autonomous systems. We discuss possible applications of the EnFPF to climate ensembles and to turbulence modeling.