
Inverse Problems and Data Assimilation
Cambridge University Press
Published on 10. August 2023
Book
Hardback
228 pages
978-1-009-41432-6 (ISBN)
Description
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.
More details
Series
Language
English
Place of publication
Cambridge
United Kingdom
Product notice
sewn/stitched
Cloth over boards
Illustrations
Worked examples or Exercises
Dimensions
Height: 229 mm
Width: 152 mm
Thickness: 14 mm
Weight
476 gr
ISBN-13
978-1-009-41432-6 (9781009414326)
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Schweitzer Classification
Other editions
Additional editions

Daniel Sanz-Alonso | Andrew Stuart | Armeen Taeb
Inverse Problems and Data Assimilation
Book
08/2023
Cambridge University Press
€38.50
Available immediately

Daniel Sanz-Alonso | Andrew Stuart | Armeen Taeb
Inverse Problems and Data Assimilation
E-Book
08/2023
Cambridge University Press
€36.99
Available for download
Persons
Daniel Sanz-Alonso is Assistant Professor in the Committee on Computational and Applied Mathematics within the Department of Statistics at the University of Chicago. His contributions to inverse problems and data assimilation have been recognized with a José Luis Rubio de Francia prize and an NSF CAREER award.
Author
University of Chicago
California Institute of Technology
University of Washington
Content
Introduction; Part I. Inverse Problems: 1. Bayesian inverse problems and well-posedness; 2. The linear-Gaussian setting; 3. Optimization perspective; 4. Gaussian approximation; 5. Monte Carlo sampling and importance sampling; 6. Markov chain Monte Carlo; Exercises for Part I; Part II. Data Assimilation: 7. Filtering and smoothing problems and well-posedness; 8. The Kalman filter and smoother; 9. Optimization for filtering and smoothing: 3DVAR and 4DVAR; 10. The extended and ensemble Kalman filters; 11. Particle filter; 12. Optimal particle filter; Exercises for Part II; Part III. Kalman Inversion: 13. Blending inverse problems and data assimilation; References; Index.