Inverse Problems and Data Assimilation: 107 (London Mathematical Society Student Texts, Series Number 107) - Tapa blanda

Sanz-Alonso, Daniel

 
9781009414296: Inverse Problems and Data Assimilation: 107 (London Mathematical Society Student Texts, Series Number 107)
Tapa blanda
ISBN 10: 1009414291 ISBN 13: 9781009414296
Editorial: Cambridge University Press, 2023

Sinopsis

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.

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Acerca del autor

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. Andrew Stuart is Professor in the Computing and Mathematical Sciences Department within the Division of Engineering and Applied Sciences at Caltech. He is well known for his work in applied and computational mathematics, in the areas of dynamical systems, inverse problems, data assimilation, and machine learning. Armeen Taeb is Assistant Professor in the Department of Statistics at the University of Washington. His work focuses on developing efficient methods for graphical modeling and latent-variable modeling, learning causal relations from data, and model selection in contemporary data analysis settings. His PhD thesis received the W. P. Carey & Co. Prize for outstanding dissertation in applied mathematics.

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Editorial
Cambridge University Press
Año de publicación
2023
Idioma
Inglés
ISBN 10 
1009414291
ISBN 13 
9781009414296
Encuadernación
Tapa blanda
Número de páginas
228
Contacto del fabricante
no disponible
Persona responsable
gpsr@libri.de
gpsr@libri.de

Europaallee 1
Bad Hersfeld
36244
Alemania

Otras ediciones populares con el mismo título

9781009414326: Inverse Problems and Data Assimilation: 107 (London Mathematical Society Student Texts, Series Number 107)

Edición Destacada

ISBN 10:  1009414321 ISBN 13:  9781009414326
Editorial: Cambridge University Press, 2023
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