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

Sanz-Alonso, Daniel

ISBN 10: 1009414291 / ISBN 13: 9781009414296

Published by Cambridge University Press (edition 1), 2023

About this Item

It's a preowned item in good condition and includes all the pages. It may have some general signs of wear and tear, such as markings, highlighting, slight damage to the cover, minimal wear to the binding, etc., but they will not affect the overall reading experience.

Seller Inventory # 1009414291-11-1

Bibliographic Details

• Title: Inverse Problems and Data Assimilation (London Mathematical Society Student Texts, Series Number 107)
• Publisher: Cambridge University Press (edition 1)
• Publication Date: 2023
• Language: English
• ISBN 10: 1009414291
• ISBN 13: 9781009414296
• Binding: Paperback
• Edition: 1.
• Condition: Good

About this title

Synopsis

A clear and concise mathematical introduction to the subjects of inverse problems and data assimilation, and their inter-relations.

About the Authors

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.