000 02371 a2200277 4500
003 NISER
005 20260107105935.0
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020 _a9781009414296
_qPaperback
040 _aNISER LIBRARY
_beng
_cNISER LIBRARY
082 0 4 _a517.972.7
_bSAN-I
100 1 _aSanz-Alonso, Daniel
245 1 0 _aInverse problems and data assimilation
260 _aNew York, NY, USA :
_bCambridge University Press,
_c2023.
300 _axvi, 210 pages ;
_c24 cm.
490 _aLondon Mathematical Society student texts ;
_v107
504 _aIncludes bibliographical references (pages 192-204) and index.
520 _aThis 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.
650 0 _aInverse problems (Differential equations)
_vTextbooks
650 0 _aMathematical models
_vTextbooks
700 1 _aStuart, Andrew
700 1 _aTaeb, Armeen
856 4 1 _3Table of contents
_uhttp://assets.cambridge.org/97810094/14326/toc/9781009414326_toc.pdf
856 4 1 _3Reviews
_uhttps://www.goodreads.com/book/show/171747708-inverse-problems-and-data-assimilation?ref=nav_sb_ss_1_13#CommunityReviews
942 _cBK
_2udc
999 _c36688
_d36688