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

Daniel Sanz-Alonso,Andrew Stuart,Armeen Taeb

ISBN 10: 1009414291 / ISBN 13: 9781009414296

Edité par Cambridge University Press -, 2023

A propos de cet article

N° de réf. du vendeur 6666-GRD-9781009414296

Détails bibliographiques

• Titre: Inverse Problems and Data Assimilation (London Mathematical Society Student Texts, Series Number 107)
• Éditeur: Cambridge University Press -
• Date de parution: 2023
• Langue: anglais
• ISBN à 10 chiffres: 1009414291
• ISBN à 13 chiffres: 9781009414296
• Reliure: Paperback
• État de l'article: New

A propos de ce titre

Synopsis

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.

À propos des auteurs

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.