ISBN: 9781032752112 - advances in high-order sensitivity analysis (advances in applied mathematics) (19 resultados)

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Hardback. Condición: New. The high-order sensitivities of model responses with respect to model parameters are notoriously difficult to compute for large-scale models involving many parameters. The neglect of higher-order response sensitivities leads to substantial errors in predicting the moments (expectation, variance, skewness, kurtosis, and higher-order) of the model response's distribution in the phase space of model parameters. The author expands on his theory of addressing high-order sensitivity analysis in this book, Advances in High-Order Sensitivity Analysis.The mathematical/computational models of physical systems comprise parameters, independent variables, and dependent variables. Since the physical processes themselves are seldom known precisely and since most of the model's parameters stem from experimental procedures that are also subject to imprecision and/or uncertainties, the results predicted by these models are also imprecise, being affected by the uncertainties underlying the respective model.In the particular case of sensitivity analysis using conventional methods, the number of large-scale computations increases exponentially. For large-scale models involving many parameters, even the first-order sensitivities are computationally very expensive to determine accurately by conventional methods. Furthermore, the "curse of dimensionality" prohibits the accurate computation of higher-order sensitivities by conventional methods.Other books by the author, all published by CRC Press, include Sensitivity and Uncertainty Analysis, Volume I: Theory (2003); Sensitivity and Uncertainty Analysis, Volume II: Applications to Large-Scale Systems (Cacuci, et al., 2005); Computational Methods for Data Evaluation and Assimilation (Cacuci et al. 2014); The Second-Order Adjoint Sensitivity Analysis Methodology (2018); and Advances in High-Order Predictive Modeling Methodologies and Illustrative Problems (2025).…

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Hardback. Condición: New. The high-order sensitivities of model responses with respect to model parameters are notoriously difficult to compute for large-scale models involving many parameters. The neglect of higher-order response sensitivities leads to substantial errors in predicting the moments (expectation, variance, skewness, kurtosis, and higher-order) of the model response's distribution in the phase space of model parameters. The author expands on his theory of addressing high-order sensitivity analysis in this book, Advances in High-Order Sensitivity Analysis.The mathematical/computational models of physical systems comprise parameters, independent variables, and dependent variables. Since the physical processes themselves are seldom known precisely and since most of the model's parameters stem from experimental procedures that are also subject to imprecision and/or uncertainties, the results predicted by these models are also imprecise, being affected by the uncertainties underlying the respective model.In the particular case of sensitivity analysis using conventional methods, the number of large-scale computations increases exponentially. For large-scale models involving many parameters, even the first-order sensitivities are computationally very expensive to determine accurately by conventional methods. Furthermore, the "curse of dimensionality" prohibits the accurate computation of higher-order sensitivities by conventional methods.Other books by the author, all published by CRC Press, include Sensitivity and Uncertainty Analysis, Volume I: Theory (2003); Sensitivity and Uncertainty Analysis, Volume II: Applications to Large-Scale Systems (Cacuci, et al., 2005); Computational Methods for Data Evaluation and Assimilation (Cacuci et al. 2014); The Second-Order Adjoint Sensitivity Analysis Methodology (2018); and Advances in High-Order Predictive Modeling Methodologies and Illustrative Problems (2025).…

Hardcover. Condición: new. Hardcover. The high-order sensitivities of model responses with respect to model parameters are notoriously difficult to compute for large-scale models involving many parameters. The neglect of higher-order response sensitivities leads to substantial errors in predicting the moments (expectation, variance, skewness, kurtosis, and higher-order) of the model responses distribution in the phase space of model parameters. The author expands on his theory of addressing high-order sensitivity analysis in this book, Advances in High-Order Sensitivity Analysis.The mathematical/computational models of physical systems comprise parameters, independent variables, and dependent variables. Since the physical processes themselves are seldom known precisely and since most of the models parameters stem from experimental procedures that are also subject to imprecision and/or uncertainties, the results predicted by these models are also imprecise, being affected by the uncertainties underlying the respective model.In the particular case of sensitivity analysis using conventional methods, the number of large-scale computations increases exponentially. For large-scale models involving many parameters, even the first-order sensitivities are computationally very expensive to determine accurately by conventional methods. Furthermore, the curse of dimensionality prohibits the accurate computation of higher-order sensitivities by conventional methods.Other books by the author, all published by CRC Press, include Sensitivity and Uncertainty Analysis, Volume I: Theory (2003); Sensitivity and Uncertainty Analysis, Volume II: Applications to Large-Scale Systems (Cacuci, et al., 2005); Computational Methods for Data Evaluation and Assimilation (Cacuci et al. 2014); The Second-Order Adjoint Sensitivity Analysis Methodology (2018); and Advances in High-Order Predictive Modeling Methodologies and Illustrative Problems (2025). This book follows the adjoint method of sensitivity analysis conceived by the author as the most efficient method for computing exactly first-order sensitivities. It will be of interest to postgraduate and professional mathematicians as well as engineers and scientists. This item is printed on demand. Shipping may be from multiple locations in the US or from the UK, depending on stock availability.…

Hardcover. Condición: new. Hardcover. The high-order sensitivities of model responses with respect to model parameters are notoriously difficult to compute for large-scale models involving many parameters. The neglect of higher-order response sensitivities leads to substantial errors in predicting the moments (expectation, variance, skewness, kurtosis, and higher-order) of the model responses distribution in the phase space of model parameters. The author expands on his theory of addressing high-order sensitivity analysis in this book, Advances in High-Order Sensitivity Analysis.The mathematical/computational models of physical systems comprise parameters, independent variables, and dependent variables. Since the physical processes themselves are seldom known precisely and since most of the models parameters stem from experimental procedures that are also subject to imprecision and/or uncertainties, the results predicted by these models are also imprecise, being affected by the uncertainties underlying the respective model.In the particular case of sensitivity analysis using conventional methods, the number of large-scale computations increases exponentially. For large-scale models involving many parameters, even the first-order sensitivities are computationally very expensive to determine accurately by conventional methods. Furthermore, the curse of dimensionality prohibits the accurate computation of higher-order sensitivities by conventional methods.Other books by the author, all published by CRC Press, include Sensitivity and Uncertainty Analysis, Volume I: Theory (2003); Sensitivity and Uncertainty Analysis, Volume II: Applications to Large-Scale Systems (Cacuci, et al., 2005); Computational Methods for Data Evaluation and Assimilation (Cacuci et al. 2014); The Second-Order Adjoint Sensitivity Analysis Methodology (2018); and Advances in High-Order Predictive Modeling Methodologies and Illustrative Problems (2025). This book follows the adjoint method of sensitivity analysis conceived by the author as the most efficient method for computing exactly first-order sensitivities. It will be of interest to postgraduate and professional mathematicians as well as engineers and scientists. This item is printed on demand. Shipping may be from our UK warehouse or from our Australian or US warehouses, depending on stock availability.…

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Condición: New. Dieser Artikel ist ein Print on Demand Artikel und wird nach Ihrer Bestellung fuer Sie gedruckt. Dan Gabriel Cacuci is a Distinguished Professor Emeritus in the Department of Mechanical Engineering at the University of South Carolina and the Karlsruhe Institute of Technology, Germany. He received his PhD in applied physics, mechanic. …

Hardcover. Condición: new. Hardcover. The high-order sensitivities of model responses with respect to model parameters are notoriously difficult to compute for large-scale models involving many parameters. The neglect of higher-order response sensitivities leads to substantial errors in predicting the moments (expectation, variance, skewness, kurtosis, and higher-order) of the model responses distribution in the phase space of model parameters. The author expands on his theory of addressing high-order sensitivity analysis in this book, Advances in High-Order Sensitivity Analysis.The mathematical/computational models of physical systems comprise parameters, independent variables, and dependent variables. Since the physical processes themselves are seldom known precisely and since most of the models parameters stem from experimental procedures that are also subject to imprecision and/or uncertainties, the results predicted by these models are also imprecise, being affected by the uncertainties underlying the respective model.In the particular case of sensitivity analysis using conventional methods, the number of large-scale computations increases exponentially. For large-scale models involving many parameters, even the first-order sensitivities are computationally very expensive to determine accurately by conventional methods. Furthermore, the curse of dimensionality prohibits the accurate computation of higher-order sensitivities by conventional methods.Other books by the author, all published by CRC Press, include Sensitivity and Uncertainty Analysis, Volume I: Theory (2003); Sensitivity and Uncertainty Analysis, Volume II: Applications to Large-Scale Systems (Cacuci, et al., 2005); Computational Methods for Data Evaluation and Assimilation (Cacuci et al. 2014); The Second-Order Adjoint Sensitivity Analysis Methodology (2018); and Advances in High-Order Predictive Modeling Methodologies and Illustrative Problems (2025). This book follows the adjoint method of sensitivity analysis conceived by the author as the most efficient method for computing exactly first-order sensitivities. It will be of interest to postgraduate and professional mathematicians as well as engineers and scientists. This item is printed on demand. Shipping may be from our Sydney, NSW warehouse or from our UK or US warehouse, depending on stock availability.…

Buch. Condición: Neu. Advances in High-Order Sensitivity Analysis | Dan Gabriel Cacuci | Buch | Einband - fest (Hardcover) | Englisch | 2026 | Chapman and Hall/CRC | EAN 9781032752112 | Verantwortliche Person für die EU: Libri GmbH, Europaallee 1, 36244 Bad Hersfeld, gpsr[at]libri[dot]de | Anbieter: preigu Print on Demand. …

Buch. Condición: Neu. nach der Bestellung gedruckt Neuware - Printed after ordering - This book follows the 'adjoint method' of sensitivity analysis conceived by the author as the most efficient method for computing exactly first-order sensitivities. It will be of interest to postgraduate and professional mathematicians as well as engineers and scientists.…