The Second-Order Adjoint Sensitivity Analysis Methodology

The Second-Order Adjoint Sensitivity Analysis Methodology
Author :
Publisher : CRC Press
Total Pages : 251
Release :
ISBN-10 : 9781351646581
ISBN-13 : 1351646583
Rating : 4/5 (81 Downloads)

Book Synopsis The Second-Order Adjoint Sensitivity Analysis Methodology by : Dan Gabriel Cacuci

Download or read book The Second-Order Adjoint Sensitivity Analysis Methodology written by Dan Gabriel Cacuci and published by CRC Press. This book was released on 2018-02-19 with total page 251 pages. Available in PDF, EPUB and Kindle. Book excerpt: The Second-Order Adjoint Sensitivity Analysis Methodology generalizes the First-Order Theory presented in the author’s previous books published by CRC Press. This breakthrough has many applications in sensitivity and uncertainty analysis, optimization, data assimilation, model calibration, and reducing uncertainties in model predictions. The book has many illustrative examples that will help readers understand the complexity of the subject and will enable them to apply this methodology to problems in their own fields. Highlights: • Covers a wide range of needs, from graduate students to advanced researchers • Provides a text positioned to be the primary reference for high-order sensitivity and uncertainty analysis • Applies to all fields involving numerical modeling, optimization, quantification of sensitivities in direct and inverse problems in the presence of uncertainties. About the Author: Dan Gabriel Cacuci is a South Carolina SmartState Endowed Chair Professor and the Director of the Center for Nuclear Science and Energy, Department of Mechanical Engineering at the University of South Carolina. He has a Ph.D. in Applied Physics, Mechanical and Nuclear Engineering from Columbia University. He is also the recipient of many awards including four honorary doctorates, the Ernest Orlando Lawrence Memorial award from the U.S. Dept. of Energy and the Arthur Holly Compton, Eugene P. Wigner and the Glenn Seaborg Awards from the American Nuclear Society.


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The computational models of physical systems comprise parameters, independent and dependent variables. Since the physical processes themselves are seldom known