An Introduction to the Mathematical Theory of Inverse Problems

An Introduction to the Mathematical Theory of Inverse Problems
Author :
Publisher : Springer Science & Business Media
Total Pages : 304
Release :
ISBN-10 : 038794530X
ISBN-13 : 9780387945309
Rating : 4/5 (309 Downloads)

Book Synopsis An Introduction to the Mathematical Theory of Inverse Problems by : Andreas Kirsch

Download or read book An Introduction to the Mathematical Theory of Inverse Problems written by Andreas Kirsch and published by Springer Science & Business Media. This book was released on 1996-09-26 with total page 304 pages. Available in PDF, EPUB and Kindle. Book excerpt: Following Keller [119] we call two problems inverse to each other if the for mulation of each of them requires full or partial knowledge of the other. By this definition, it is obviously arbitrary which of the two problems we call the direct and which we call the inverse problem. But usually, one of the problems has been studied earlier and, perhaps, in more detail. This one is usually called the direct problem, whereas the other is the inverse problem. However, there is often another, more important difference between these two problems. Hadamard (see [91]) introduced the concept of a well-posed problem, originating from the philosophy that the mathematical model of a physical problem has to have the properties of uniqueness, existence, and stability of the solution. If one of the properties fails to hold, he called the problem ill-posed. It turns out that many interesting and important inverse in science lead to ill-posed problems, while the corresponding di problems rect problems are well-posed. Often, existence and uniqueness can be forced by enlarging or reducing the solution space (the space of "models"). For restoring stability, however, one has to change the topology of the spaces, which is in many cases impossible because of the presence of measurement errors. At first glance, it seems to be impossible to compute the solution of a problem numerically if the solution of the problem does not depend continuously on the data, i. e. , for the case of ill-posed problems.


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