Inverse problems and data assimilation

• Verfasst von: Sanz-Alonso, Daniel [VerfasserIn]
• Verfasst von: Stuart, Andrew [VerfasserIn]
• Verfasst von: Taeb, Armeen [VerfasserIn]
• Titel: Inverse problems and data assimilation
• Verf.angabe: Daniel Sanz-Alonso (University of Chicago), Andrew Stuart (California Institute of Technology), Armeen Taeb (University of Washington)
• Verlagsort: Cambridge ; New York, NY ; Port Melbourne ; New Delhi ; Singapore
• Verlag: Cambridge University Press
• Jahr: 2023
• Umfang: 1 Online-Ressource (xvi, 210 Seiten)
• Illustrationen: Diagramme
• Gesamttitel/Reihe: London Mathematical Society student texts ; 107
• Fussnoten: Title from publisher's bibliographic system (viewed on 31 Jul 2023)
• ISBN: 978-1-009-41431-9
• DOI: doi:10.1017/9781009414319
• Schlagwörter: (s)Inverses Problem / (s)Datenassimilation / (s)Bayes-Verfahren / (s)Gauß-Approximation / (s)Markov-Ketten-Monte-Carlo-Verfahren / (s)Kalman-Filter / (s)Sequenzielle Monte-Carlo-Methode
• Datenträger: Online-Ressource
• Sprache: eng
• Bibliogr. Hinweis: Erscheint auch als : Druck-Ausgabe: Sanz-Alonso, Daniel: Inverse problems and data assimilation. - Cambridge : Cambridge University Press, 2023. - xvi, 210 Seiten
• Sach-SW: Angewandte Informatik
• Sach-SW: COMPUTERS / General
• Sach-SW: Datenwissenschaft und -analyse: allgemein
• Sach-SW: Informationstheorie
• Sach-SW: Maschinelles Lernen
• Sach-SW: Meteorologie und Klimatologie(Klimaforschung)
• Sach-SW: Numerical analysis
• Sach-SW: Theoretische Informatik
• K10plus-PPN: 1857896564
• K10plus-PPN:
  Universitätsbibliothek
• Lokale URL UB: Zum Volltext

Abstract

This concise introduction provides an entry point to the world of inverse problems and data assimilation for advanced undergraduates and beginning graduate students in the mathematical sciences. It will also appeal to researchers in science and engineering who are interested in the systematic underpinnings of methodologies widely used in their disciplines. The authors examine inverse problems and data assimilation in turn, before exploring the use of data assimilation methods to solve generic inverse problems by introducing an artificial algorithmic time. Topics covered include maximum a posteriori estimation, (stochastic) gradient descent, variational Bayes, Monte Carlo, importance sampling and Markov chain Monte Carlo for inverse problems; and 3DVAR, 4DVAR, extended and ensemble Kalman filters, and particle filters for data assimilation. The book contains a wealth of examples and exercises, and can be used to accompany courses as well as for self-study.