Second-Order Adjoint Sensitivity Analysis Methodology

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A01=Dan Gabriel Cacuci
Adjoint Functions
Adjoint Neutron
Adjoint Operators
Adjoint Sensitivity
advanced sensitivity optimization methods
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Atmospheric Chemical Transport Models
Author_Dan Gabriel Cacuci
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Computed Responses
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Covariance Matrices
Covariance Matrix
Data Assimilation
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detector response analysis
Engineering Systems
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eq_nobargain
Fourth Order Moment
General Nonlinear Systems
graduate level textbook
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inverse problem solutions
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Large Scale Computations
Large Scale Systems
Model Calibration Problem
Nominal Parameter Values
Non-linear Systems
Normed Linear Space
numerical modeling techniques
optimization algorithms
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Parameter Covariance Matrix
Partial Sensitivities
Performing Sensitivity Analysis
Physical Systems
Predicted Covariance Matrix
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Reduced Parameters Uncertainties
Response Sensitivity
Sensitivity Analysis
softlaunch
Symmetric Positive Matrix
Time Node
Uncertain quantification
uncertainty quantification

Product details

  • ISBN 9781498726481
  • Weight: 614g
  • Dimensions: 156 x 234mm
  • Publication Date: 28 Feb 2018
  • Publisher: Taylor & Francis Inc
  • Publication City/Country: US
  • Product Form: Hardback
  • Language: English
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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.

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.