Details
Computational Methods for Applied Inverse Problems
ISSN, Band 56 1. Aufl.
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210,00 € |
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| Verlag: | De Gruyter |
| Format: | |
| Veröffentl.: | 30.10.2012 |
| ISBN/EAN: | 9783110259056 |
| Sprache: | englisch |
| Anzahl Seiten: | 550 |
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Beschreibungen
<p>Nowadays inverse problems and applications in science and engineering represent an extremely active research field. The subjects are related to mathematics, physics, geophysics, geochemistry, oceanography, geography and remote sensing, astronomy, biomedicine, and other areas of applications.</p>
<p>This monograph reports recent advances of inversion theory and recent developments with practical applications in frontiers of sciences, especially inverse design and novel computational methods for inverse problems. The practical applications include inverse scattering, chemistry, molecular spectra data processing, quantitative remote sensing inversion, seismic imaging, oceanography, and astronomical imaging.</p>
<p>The book serves as a reference book and readers who do research in applied mathematics, engineering, geophysics, biomedicine, image processing, remote sensing, and environmental science will benefit from the contents since the book incorporates a background of using statistical and non-statistical methods, e.g., regularization and optimization techniques for solving practical inverse problems.</p>
<p>This monograph reports recent advances of inversion theory and recent developments with practical applications in frontiers of sciences, especially inverse design and novel computational methods for inverse problems. The practical applications include inverse scattering, chemistry, molecular spectra data processing, quantitative remote sensing inversion, seismic imaging, oceanography, and astronomical imaging.</p>
<p>The book serves as a reference book and readers who do research in applied mathematics, engineering, geophysics, biomedicine, image processing, remote sensing, and environmental science will benefit from the contents since the book incorporates a background of using statistical and non-statistical methods, e.g., regularization and optimization techniques for solving practical inverse problems.</p>
<p><strong>Yanfei Wang,</strong> Institute of Geology and Geophysics, Chinese Academy of Sciences, Beijing, China; <strong>Anatoly G. Yagola,</strong> Lomonosov Moscow State University, Russia; <strong>Changchun Yang,</strong> Institute of Geology and Geophysics, Chinese Academy of Sciences, Beijing, China.</p>
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