Daniel Sanz-Alonso (University of Chicago), Andrew Stuart (California Institute of Technology), Armeen Taeb (University of Washington)

Inverse Problems and Data Assimilation

• Broschiertes Buch

Produktbeschreibung

This concise introduction covers inverse problems and data assimilation, before exploring their inter-relations. Suitable for both classroom teaching and self-guided study, it is aimed at advanced undergraduates and beginning graduate students in mathematical sciences, together with researchers in science and engineering.

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Produktdetails

• London Mathematical Society Student Texts
• Verlag: Cambridge University Press
• Seitenzahl: 221
• Erscheinungstermin: 10. August 2023
• Englisch
• Abmessung: 226mm x 150mm x 14mm
• Gewicht: 346g
• ISBN-13: 9781009414296
• ISBN-10: 1009414291
• Artikelnr.: 68271008

Autorenporträt

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.

Inhaltsangabe

Introduction
Part I. Inverse Problems: 1. Bayesian inverse problems and well-posedness
2. The linear-Gaussian setting
3. Optimization perspective
4. Gaussian approximation
5. Monte Carlo sampling and importance sampling
6. Markov chain Monte Carlo
Exercises for Part I
Part II. Data Assimilation: 7. Filtering and smoothing problems and well-posedness
8. The Kalman filter and smoother
9. Optimization for filtering and smoothing: 3DVAR and 4DVAR
10. The extended and ensemble Kalman filters
11. Particle filter
12. Optimal particle filter
Exercises for Part II
Part III. Kalman Inversion: 13. Blending inverse problems and data assimilation
References
Index.