Multilevel Ensemble Kalman Filtering based on a sample average of independent EnKF estimators
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Type
ArticleKAUST Department
Applied Mathematics and Computational Science ProgramComputer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division
KAUST Grant Number
URF/1/2584-01-01Date
2020Embargo End Date
2022-01-27Permanent link to this record
http://hdl.handle.net/10754/661369
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We introduce a new multilevel ensemble Kalman filter method (MLEnKF) which consists of a hierarchy of independent samples of ensemble Kalman filters (EnKF). This new MLEnKF method is fundamentally different from the preexisting method introduced by Hoel, Law and Tempone in 2016, and it is suitable for extensions towards multi-index Monte Carlo based filtering methods. Robust theoretical analysis and supporting numerical examples show that under appropriate regularity assumptions, the MLEnKF method has better complexity than plain vanilla EnKF in the large-ensemble and fine-resolution limits, for weak approximations of quantities of interest. The method is developed for discrete-time filtering problems with finite-dimensional state space and linear observations polluted by additive Gaussian noise.Citation
Hoel, H., Shaimerdenova, G., & Tempone, R. (2020). Multilevel Ensemble Kalman Filtering based on a sample average of independent EnKF estimators. Foundations of Data Science, 2(4), 351–390. doi:10.3934/fods.2020017Sponsors
This work was funded by the KAUST Office of Sponsored Research (OSR) under Award No. URF/1/2584-01-01; the Alexander von Humboldt Foundation; the Federal Ministry of Education and Research (BMBF) and the Ministry of Culture and Science of the German State of North Rhine-Westphalia (MKW) under the Excellence Strategy of the Federal Government and the Länder. G.Shaimerdenova and R. Tempone are members of the KAUST SRI Center for Uncertainty Quantification in Computational Science and Engineering.Journal
Foundations of Data SciencearXiv
2002.00480Additional Links
http://aimsciences.org//article/doi/10.3934/fods.2020017Relations
Is Supplemented By:- [Software]
Title: GaukharSH/mlenkf: Julia Implementation of Multilevel Ensemble Kalman Filtering (MLEnKF) with Local Kalman Gains. Publication Date: 2019-12-22. github: GaukharSH/mlenkf Handle: 10754/667585
ae974a485f413a2113503eed53cd6c53
10.3934/fods.2020017