KAUST DepartmentApplied Mathematics and Computational Science Program
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AbstractThis work embeds a multilevel Monte Carlo sampling strategy into the Monte Carlo step of the ensemble Kalman filter (EnKF) in the setting of finite dimensional signal evolution and noisy discrete-time observations. The signal dynamics is assumed to be governed by a stochastic differential equation (SDE), and a hierarchy of time grids is introduced for multilevel numerical integration of that SDE. The resulting multilevel EnKF is proved to asymptotically outperform EnKF in terms of computational cost versus approximation accuracy. The theoretical results are illustrated numerically.
CitationMultilevel ensemble Kalman filtering 2016, 54 (3):1813 SIAM Journal on Numerical Analysis
SponsorsThis research was supported by King Abdullah University of Science and Technology (KAUST). The authors were members of the SRI Center for Uncertainty Quantification at KAUST for much of the research reported.