A Langevinized Ensemble Kalman Filter for Large-Scale Static and Dynamic Learning

TL;DR

This paper introduces the Langevinized Ensemble Kalman Filter, a scalable particle filtering method that converges to the correct distribution for large-scale static and dynamic systems, enhancing uncertainty quantification.

Contribution

It reformulates the EnKF within Langevin dynamics, enabling convergence guarantees and scalability for high-dimensional and large-sample problems.

Findings

Converges to the true filtering distribution in Wasserstein distance.

Effective in high-dimensional variable selection and deep learning tasks.

Scalable to large systems with many stages and observations.

Abstract

The Ensemble Kalman Filter (EnKF) has achieved great successes in data assimilation in atmospheric and oceanic sciences, but its failure in convergence to the right filtering distribution precludes its use for uncertainty quantification. We reformulate the EnKF under the framework of Langevin dynamics, which leads to a new particle filtering algorithm, the so-called Langevinized EnKF. The Langevinized EnKF inherits the forecast-analysis procedure from the EnKF and the use of mini-batch data from the stochastic gradient Langevin-type algorithms, which make it scalable with respect to both the dimension and sample size. We prove that the Langevinized EnKF converges to the right filtering distribution in Wasserstein distance under the big data scenario that the dynamic system consists of a large number of stages and has a large number of samples observed at each stage. We reformulate the Bayesian inverse problem as a dynamic state estimation problem based on the techniques of subsampling and Langevin diffusion process. We illustrate the performance of the Langevinized EnKF using a variety of examples, including the Lorenz-96 model, high-dimensional variable selection, Bayesian deep learning, and Long Short Term Memory (LSTM) network learning with dynamic data.