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 |
|---|---|
| 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 |
