Inverse Problems and Data Assimilation
$107.28
- Paperback
221 pages
- Release Date
31 August 2023
Summary
Unlocking the Secrets of Inverse Problems and Data Assimilation
This concise introduction offers a gateway into the fascinating world of inverse problems and data assimilation, tailored for advanced undergraduates and beginning graduate students in the mathematical sciences. It’s also a valuable resource for researchers in science and engineering seeking a systematic understanding of methodologies prevalent in their fields.
The authors delve into inverse problems and data as…
Book Details
| ISBN-13: | 9781009414296 |
|---|---|
| ISBN-10: | 1009414291 |
| Series: | London Mathematical Society Student Texts |
| Author: | Andrew Stuart, Armeen Taeb, Daniel Sanz-Alonso |
| Publisher: | Cambridge University Press |
| Imprint: | Cambridge University Press |
| Format: | Paperback |
| Number of Pages: | 221 |
| Release Date: | 31 August 2023 |
| Weight: | 330g |
| Dimensions: | 226mm x 152mm x 15mm |
About The Author
Andrew Stuart
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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