The project is concerned with sequential data assimilation in the context of state estimation for stochastic processes and more generally speaking sequential learning algorithms. A closed-form solution of the associated Bayesian filtering and smoothing problems are in most cases unavailable and numerical methods such as sequential Monte Carlo (SMC) methods have to be considered. While these methods are well understood theoretically and have found numerous applications they also suffer from the curse of dimensionality. In recent years there therefore has been a growing interest in developing approximate filter and smoothing algorithms that are applicable to high-dimensional data assimilation problems such as those arising in the geoscience, numerical weather prediction and the cognitive sciences. Here we mention in particular the popular class of ensemble Kalman filters (EnKFs) and smoothers.
During the first funding period, we have investigated the time continuous limiting equations of a large class of EnKFs as well as the long-time behaviour of the resulting time-continuous, so called, ensemble Kalman-Bucy filter (EnKBF) equations. In a further line of research, we have studied mean-field formulations for the general time-continuous filtering problem including alternative numerical implementations and extensions to filtering problems with correlated noise. The extension of such mean-field approaches to discrete-time observations leads to a sequence of Schrödinger bridge (i.e. stochastic optimal control) problems. An essential building block for an efficient numerical implementation of such stochastic optimal control problems is provided by a novel class of smoothing algorithms, which have also been developed during the first funding period.
During the second funding period, the mean-field limits of the EnKBF and its nonlinear extensions, such as the feedback particle filter, will be systematically studied in terms of well-posedness and asymptotic consistency. The long-time accuracy and stability as well as the computational efficiency of finite ensemble-size implementations will also be further investigated. A second main line of research is provided by the newly proposed Schrödinger approach to the assimilation of discrete-time observations. Since the computation of Schrödinger bridges and their associated control laws is computationally demanding a main focus will be on alternative, more efficient implementations using previous results from this project and project A06 on gradient log-density estimators.
The project is central for many research topics of the CRC and will closely interact in particular with projects A01, A03, A06, B03, B06, B08, and B09.
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Pathiraja, S., & van Leeuwen, P. J. (2022). Multiplicative non-Gaussian model error estimation in data assimilation. Journal of Advances in Modeling Earth Systems, 14, e2021MS002564. https://doi.org/10.1029/2021MS002564
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de Wiljes, J., Reich, S. and Stannat, W. (2018). Long-Time Stability and Accuracy of the Ensemble Kalman--Bucy Filter for Fully Observed Processes and Small Measurement Noise. SIAM Journal on Applied Dynamical Systems, 17(2), 1152-1181. arXiv: 1612.06065; doi: 10.1137/17M1119056
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