Overview
Introduces pioneering results of the authors’ own experiments on coefficient inverse problems
Provides recipes for numerical implementations of developed algorithms
Demonstrates performance of algorithms in both synthetic and experimental data
Includes supplementary material: sn.pub/extras
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Table of contents (6 chapters)
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Front Matter
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Back Matter
Keywords
About this book
Two central questions for CIPs are addressed: How to obtain a good approximations for the exact solution without any knowledge of a small neighborhood of this solution, and how to refine it given the approximation.
The book also combines analytical convergence results with recipes for various numerical implementations of developed algorithms. The developed technique is applied to two types of blind experimental data, which are collected both in a laboratory and in the field. The result for the blind backscattering experimental data collected in the field addresses a real world problem of imaging of shallow explosives.
Authors and Affiliations
Bibliographic Information
Book Title: Approximate Global Convergence and Adaptivity for Coefficient Inverse Problems
Authors: Larisa Beilina, Michael Victor Klibanov
DOI: https://doi.org/10.1007/978-1-4419-7805-9
Publisher: Springer New York, NY
eBook Packages: Mathematics and Statistics, Mathematics and Statistics (R0)
Copyright Information: Springer Science+Business Media, LLC 2012
Hardcover ISBN: 978-1-4419-7804-2Published: 09 March 2012
Softcover ISBN: 978-1-4899-9530-8Published: 13 April 2014
eBook ISBN: 978-1-4419-7805-9Published: 09 March 2012
Edition Number: 1
Number of Pages: XVI, 408
Topics: Partial Differential Equations, Numerical and Computational Physics, Simulation, Mathematical and Computational Engineering, Numerical Analysis, Global Analysis and Analysis on Manifolds