TY  - GEN
AU  - Sanz-Alonso,Daniel
AU  - Stuart,Andrew
AU  - Taeb,Armeen
TI  - Inverse problems and data assimilation
T2  - London Mathematical Society student texts
SN  - 9781009414296
U1  - 517.972.7 
PY  - 2023///
CY  - New York, NY, USA
PB  - Cambridge University Press
KW  - Inverse problems (Differential equations)
KW  - Textbooks
KW  - Mathematical models
N1  - Includes bibliographical references (pages 192-204) and index
N2  - 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. Provides a gentle introduction to inverse problems and data assimilation emphasizing the unity between both subjects and the potential for an exchange of ideas between them. Includes numerous pointers to the wider literature
Features examples and exercises for classroom teaching and self-guided learning.
UR  - http://assets.cambridge.org/97810094/14326/toc/9781009414326_toc.pdf
UR  - https://www.goodreads.com/book/show/171747708-inverse-problems-and-data-assimilation?ref=nav_sb_ss_1_13#CommunityReviews
ER  -