Coupling Techniques for Nonlinear Ensemble Filtering
Name
20m1312204 (1).pdf
Description
Published version
Size
1.25 MB
Format
Adobe PDF
Checksum (MD5)
8411e194148cf4f031de420dcdb33246
Author(s)
Spantini, Alessio
Baptista, Ricardo
Marzouk, Youssef
Date Issued
November 2022
Journal
SIAM Review
Publisher
Society for Industrial & Applied Mathematics (SIAM)
Citation
Spantini, Alessio, Baptista, Ricardo and Marzouk, Youssef. 2022. "Coupling Techniques for Nonlinear Ensemble Filtering." SIAM Review, 64 (4).
Version
Final published version
Abstract
We consider filtering in high-dimensional non-Gaussian state-space models with intractable transition kernels, nonlinear and possibly chaotic dynamics, and sparse observations in space and time. We propose a novel filtering methodology that harnesses transportation of measures, convex optimization, and ideas from probabilistic graphical models to yield robust ensemble approximations of the filtering distribution in high dimensions. Our approach can be understood as the natural generalization of the ensemble Kalman filter (EnKF) to nonlinear updates, using stochastic or deterministic couplings. The use of nonlinear updates can reduce the intrinsic bias of the EnKF at a marginal increase in computational cost. We avoid any form of importance sampling and introduce non-Gaussian localization approaches for dimension scalability. Our framework achieves state-of-the-art tracking performance on challenging configurations of the Lorenz-96 model in the chaotic regime.
MIT Department
Massachusetts Institute of Technology. Department of Aeronautics and Astronautics
Terms of Use
Article is made available in accordance with the publisher's policy and may be subject to US copyright law. Please refer to the publisher's site for terms of use.
Persistent DSpace Link
DOI of Published Version
https://doi.org/10.1137/20M1312204
