Uncertainty Quantification and stochastic modeling with Matlab:
Saved in:
| Main Author: | |
|---|---|
| Format: | Electronic eBook |
| Language: | English |
| Published: |
London
ISTE Press Ltd
2015
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| Subjects: | |
| Online Access: | Volltext |
| Item Description: | Includes bibliographical references and index Uncertainty Quantification (UQ) is a relatively new research area which describes the methods and approaches used to supply quantitative descriptions of the effects of uncertainty, variability and errors in simulation problems and models. It is rapidly becoming a field of increasing importance, with many real-world applications within statistics, mathematics, probability and engineering, but also within the natural sciences. Literature on the topic has up until now been largely based on polynomial chaos, which raises difficulties when considering different types of approximation and does not lead to a unified presentation of the methods. Moreover, this description does not consider either deterministic problems or infinite dimensional ones. This book gives a unified, practical and comprehensive presentation of the main techniques used for the characterization of the effect of uncertainty on numerical models and on their exploitation in numerical problems. In particular, applications to linear and nonlinear systems of equations, differential equations, optimization and reliability are presented. Applications of stochastic methods to deal with deterministic numerical problems are also discussed. Matlabª illustrates the implementation of these methods and makes the book suitable as a textbook and for self-study |
| Physical Description: | 1 Online-Ressource (xiii, 442 pages) |
| ISBN: | 9780081004715 0081004710 9781785480058 1785480057 |
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| 500 | |a Uncertainty Quantification (UQ) is a relatively new research area which describes the methods and approaches used to supply quantitative descriptions of the effects of uncertainty, variability and errors in simulation problems and models. It is rapidly becoming a field of increasing importance, with many real-world applications within statistics, mathematics, probability and engineering, but also within the natural sciences. Literature on the topic has up until now been largely based on polynomial chaos, which raises difficulties when considering different types of approximation and does not lead to a unified presentation of the methods. Moreover, this description does not consider either deterministic problems or infinite dimensional ones. This book gives a unified, practical and comprehensive presentation of the main techniques used for the characterization of the effect of uncertainty on numerical models and on their exploitation in numerical problems. In particular, applications to linear and nonlinear systems of equations, differential equations, optimization and reliability are presented. Applications of stochastic methods to deal with deterministic numerical problems are also discussed. Matlabª illustrates the implementation of these methods and makes the book suitable as a textbook and for self-study | ||
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Record in the Search Index
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| author | Cursi, Eduardo Souza de |
| author_facet | Cursi, Eduardo Souza de |
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| dewey-full | 519.2 |
| dewey-hundreds | 500 - Natural sciences and mathematics |
| dewey-ones | 519 - Probabilities and applied mathematics |
| dewey-raw | 519.2 |
| dewey-search | 519.2 |
| dewey-sort | 3519.2 |
| dewey-tens | 510 - Mathematics |
| discipline | Mathematik |
| format | Electronic eBook |
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| isbn | 9780081004715 0081004710 9781785480058 1785480057 |
| language | English |
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| spelling | Cursi, Eduardo Souza de Verfasser aut Uncertainty Quantification and stochastic modeling with Matlab London ISTE Press Ltd 2015 1 Online-Ressource (xiii, 442 pages) txt rdacontent c rdamedia cr rdacarrier Includes bibliographical references and index Uncertainty Quantification (UQ) is a relatively new research area which describes the methods and approaches used to supply quantitative descriptions of the effects of uncertainty, variability and errors in simulation problems and models. It is rapidly becoming a field of increasing importance, with many real-world applications within statistics, mathematics, probability and engineering, but also within the natural sciences. Literature on the topic has up until now been largely based on polynomial chaos, which raises difficulties when considering different types of approximation and does not lead to a unified presentation of the methods. Moreover, this description does not consider either deterministic problems or infinite dimensional ones. This book gives a unified, practical and comprehensive presentation of the main techniques used for the characterization of the effect of uncertainty on numerical models and on their exploitation in numerical problems. In particular, applications to linear and nonlinear systems of equations, differential equations, optimization and reliability are presented. Applications of stochastic methods to deal with deterministic numerical problems are also discussed. Matlabª illustrates the implementation of these methods and makes the book suitable as a textbook and for self-study MATHEMATICS / Applied bisacsh MATHEMATICS / Probability & Statistics / General bisacsh Stochastic models fast Uncertainty (Information theory) fast Stochastic partial differential equations Stochastic models Uncertainty (Information theory) MATLAB (DE-588)4329066-8 gnd rswk-swf Mathematik (DE-588)4037944-9 gnd rswk-swf Unsicherheit (DE-588)4186957-6 gnd rswk-swf Numerisches Modell (DE-588)4338132-7 gnd rswk-swf Unsicherheit (DE-588)4186957-6 s Mathematik (DE-588)4037944-9 s Numerisches Modell (DE-588)4338132-7 s MATLAB (DE-588)4329066-8 s DE-604 Sampaio, Rubens Sonstige oth http://www.sciencedirect.com/science/book/9781785480058 Verlag Volltext |
| spellingShingle | Cursi, Eduardo Souza de Uncertainty Quantification and stochastic modeling with Matlab MATHEMATICS / Applied bisacsh MATHEMATICS / Probability & Statistics / General bisacsh Stochastic models fast Uncertainty (Information theory) fast Stochastic partial differential equations Stochastic models Uncertainty (Information theory) MATLAB (DE-588)4329066-8 gnd Mathematik (DE-588)4037944-9 gnd Unsicherheit (DE-588)4186957-6 gnd Numerisches Modell (DE-588)4338132-7 gnd |
| subject_GND | (DE-588)4329066-8 (DE-588)4037944-9 (DE-588)4186957-6 (DE-588)4338132-7 |
| title | Uncertainty Quantification and stochastic modeling with Matlab |
| title_auth | Uncertainty Quantification and stochastic modeling with Matlab |
| title_exact_search | Uncertainty Quantification and stochastic modeling with Matlab |
| title_full | Uncertainty Quantification and stochastic modeling with Matlab |
| title_fullStr | Uncertainty Quantification and stochastic modeling with Matlab |
| title_full_unstemmed | Uncertainty Quantification and stochastic modeling with Matlab |
| title_short | Uncertainty Quantification and stochastic modeling with Matlab |
| title_sort | uncertainty quantification and stochastic modeling with matlab |
| topic | MATHEMATICS / Applied bisacsh MATHEMATICS / Probability & Statistics / General bisacsh Stochastic models fast Uncertainty (Information theory) fast Stochastic partial differential equations Stochastic models Uncertainty (Information theory) MATLAB (DE-588)4329066-8 gnd Mathematik (DE-588)4037944-9 gnd Unsicherheit (DE-588)4186957-6 gnd Numerisches Modell (DE-588)4338132-7 gnd |
| topic_facet | MATHEMATICS / Applied MATHEMATICS / Probability & Statistics / General Stochastic models Uncertainty (Information theory) Stochastic partial differential equations MATLAB Mathematik Unsicherheit Numerisches Modell |
| url | http://www.sciencedirect.com/science/book/9781785480058 |
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