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MULTILEVEL ENSEMBLE TRANSFORM PARTICLE FILTERING
- This paper extends the multilevel Monte Carlo variance reduction technique to nonlinear filtering. In particular, multilevel Monte Carlo is applied to a certain variant of the particle filter, the ensemble transform particle filter (EPTF). A key aspect is the use of optimal transport methods to re-establish correlation between coarse and fine ensembles after resampling; this controls the variance of the estimator. Numerical examples present a proof of concept of the effectiveness of the proposed method, demonstrating significant computational cost reductions (relative to the single-level ETPF counterpart) in the propagation of ensembles.
Author details: | A. Gregory, C. J. Cotter, Sebastian ReichORCiDGND |
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DOI: | https://doi.org/10.1137/15M1038232 |
ISSN: | 1064-8275 |
ISSN: | 1095-7197 |
Title of parent work (English): | SIAM journal on scientific computing |
Publisher: | Society for Industrial and Applied Mathematics |
Place of publishing: | Philadelphia |
Publication type: | Article |
Language: | English |
Year of first publication: | 2016 |
Publication year: | 2016 |
Release date: | 2020/03/22 |
Tag: | multilevel Monte Carlo; optimal transport; sequential data assimilation |
Volume: | 38 |
Number of pages: | 22 |
First page: | A1317 |
Last Page: | A1338 |
Funding institution: | Science and Solutions to a Changing Planet DTP; Natural Environmental Research Council |
Organizational units: | Mathematisch-Naturwissenschaftliche Fakultät / Institut für Mathematik |
Peer review: | Referiert |