Multilevel ensemble Kalman filtering algorithms

26.02.2020, 10:15-11:15  –  Haus 9, Raum 0.13

Hakon Hoel (RWTH Aachen)

The ensemble Kalman filter (EnKF) is a Monte-Carlo-based sequential filtering method that is often both robust and efficient, but its performance may suffer in settings where the computational cost of accurate simulations of ensemble members/particles is high. I will present recent results [1, 2, 3] on marrying the multilevel Monte Carlo method with EnKF to obtain the multilevel ensemble Kalman filter (MLEnKF). The new method can be applied in the following filtering settings:
(I) finite-dimensional state space and discrete-time observations,
(II) infinite-dimensional state space and discrete-time, finite-dimensional observations.
Theoretical results and numerical evidence of the performance gain of MLEnKF over EnKF will be presented.

[1] H. Hoel, K. Law, and R. Tempone, Multilevel ensemble Kalman filtering, SIAM J. Numer. Anal. 54(3), 18131839, 2016.
[2] A. Chernov, H. Hoel, K. Law, F. Nobile, and R. Tempone, Multilevel ensemble Kalman filtering for spatio-temporal processes, arXiv:1710.07282, 2017.
[3] H. Hoel, G. Shaimerdenova, and R. Tempone, Multilevel Ensemble Kalman Filtering with local-level Kalman gains, arXiv:2002.00480, 2020.

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