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.
Author details: | Sebastian ReichORCiDGND |
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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 |
Publication year: | 2019 |
Release date: | 2021/05/03 |
Volume: | 28 |
Number of pages: | 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): | CC-BY - Namensnennung 4.0 International |