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Statistical variational data assimilation

Benaceur, Amina 1; Verfürth, Barbara ORCID iD icon 1
1 Institut für Angewandte und Numerische Mathematik (IANM), Karlsruher Institut für Technologie (KIT)

Abstract:

This paper is a contribution in the context of variational data assimilation combined with statistical learning. The framework of data assimilation traditionally uses data collected at sensor locations in order to bring corrections to a numerical model designed using knowledge of the physical system of interest. However, some applications do not have available data at all times, but only during an initial training phase. Hence, we suggest to combine data assimilation with statistical learning methods; namely, deep learning. More precisely, for time steps at which data is unavailable, a surrogate deep learning model runs predictions of the 'true' data which is then assimilated by the new model. In this paper, we also derive a priori error estimates on this statistical variational data assimilation (SVDA) approximation. Finally, we assess the method by numerical test cases.


Verlagsausgabe §
DOI: 10.5445/IR/1000175851
Veröffentlicht am 04.11.2024
Cover der Publikation
Zugehörige Institution(en) am KIT Institut für Angewandte und Numerische Mathematik (IANM)
Publikationstyp Zeitschriftenaufsatz
Publikationsdatum 01.12.2024
Sprache Englisch
Identifikator ISSN: 0045-7825
KITopen-ID: 1000175851
Erschienen in Computer Methods in Applied Mechanics and Engineering
Verlag Elsevier
Band 432
Heft B
Seiten 117402
Vorab online veröffentlicht am 03.10.2024
Nachgewiesen in Scopus
Dimensions
Web of Science
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