Ensemble Kalman filters are based on a Gaussian assumption, which can limit their
performance in some non-Gaussian settings. This paper reviews two nonlinear, non …

A sequential estimator based on the Ensemble Kalman Filter for Data Assimilation of fluid
flows is presented in this research work. The main feature of this estimator is that the Kalman …

We consider calculation of the Bayesian model evidence, which is an essential component
in realistic uncertainty quantification. The main motivation is large-scale porous-media-flow …

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 …

Essential features of the Multigrid Ensemble Kalman Filter (Moldovan et al.(2021)[24])
recently proposed for Data Assimilation of fluid flows are investigated and assessed in this …

The potential of sequential Data Assimilation (DA) techniques to improve the numerical
accuracy of Large Eddy Simulation (LES) performed on coarse grid is assessed …

Abstract Multi-level Monte Carlo methods have become an established technique in
uncertainty quantification as they provide the same statistical accuracy as traditional Monte …

In ensemble-based data assimilation (DA), the ensemble size is usually limited to around
one hundred. Straightforward application of ensemble-based DA can therefore result in …

In this work we combine ideas from multi-index Monte Carlo and ensemble Kalman filtering
(EnKF) to produce a highly efficient filtering method called multi-index EnKF (MIEnKF) …

In this article we consider the linear filtering problem in continuous-time. We develop and
apply multilevel Monte Carlo (MLMC) strategies for ensemble Kalman-Bucy filters (EnKBFs) …