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.
| ISBN: | 9781498726481 |
| Publication date: | 27th February 2018 |
| Author: | Dan Gabriel Cacuci |
| Publisher: | Chapman & Hall/CRC an imprint of CRC Press |
| Format: | Hardback |
| Pagination: | 306 pages |
| Series: | Advances in Applied Mathematics |
| Genres: |
Applied mathematics Mathematical physics Technology, Engineering, Agriculture, Industrial processes |
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.
The Second-Order Adjoint Sensitivity Analysis Methodology features in the following genres: Applied mathematics, Mathematical physics, Technology, Engineering, Agriculture, Industrial processes
The Second-Order Adjoint Sensitivity Analysis Methodology is available in Hardback
The Second-Order Adjoint Sensitivity Analysis Methodology was written by Dan Gabriel Cacuci and published by Chapman & Hall/CRC an imprint of CRC Press
The Second-Order Adjoint Sensitivity Analysis Methodology has 306 pages
Yes it is part of Advances in Applied Mathematics series
£131.39