Data assimilation

  • Data assimilation addresses the general problem of how to combine model-based predictions with partial and noisy observations of the process in an optimal manner. This survey focuses on sequential data assimilation techniques using probabilistic particle-based algorithms. In addition to surveying recent developments for discrete- and continuous-time data assimilation, both in terms of mathematical foundations and algorithmic implementations, we also provide a unifying framework from the perspective of coupling of measures, and Schrödinger’s boundary value problem for stochastic processes in particular.

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Metadaten
Author details:Sebastian ReichORCiDGND
DOI:https://doi.org/10.1017/S0962492919000011
ISSN:0962-4929
ISSN:1474-0508
Title of parent work (English):Acta numerica
Subtitle (English):the Schrödinger perspective
Publisher:Cambridge Univ. Press
Place of publishing:New York
Publication type:Article
Language:English
Date of first publication:2019/06/14
Completion year:2019
Release date:2021/05/03
Volume:28
Page number:77
First page:635
Last Page:711
Funding institution:Deutsche Forschungsgemeinschaft (DFG)German Research Foundation (DFG) [CRC 1294]
Organizational units:Mathematisch-Naturwissenschaftliche Fakultät / Institut für Mathematik
DDC classification:5 Naturwissenschaften und Mathematik / 51 Mathematik / 510 Mathematik
Peer review:Referiert
Publishing method:Open Access / Hybrid Open-Access
License (German):License LogoCC BY - Namensnennung, 4.0 International