Verification, validation, and uncertainty quantification in scientific computing:
Can you trust results from modeling and simulation? This text provides a framework for assessing the reliability of and uncertainty included in the results used by decision makers and policy makers in industry and government. The emphasis is on models described by PDEs and their numerical solution....
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| Main Author: | |
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| Other Authors: | |
| Format: | eBook |
| Language: | English |
| Published: |
Cambridge, United Kingdom ; New York, NY, USA
Cambridge University Press
2025
|
| Edition: | Second edition. |
| Links: | https://doi.org/10.1017/9781009031004 |
| Summary: | Can you trust results from modeling and simulation? This text provides a framework for assessing the reliability of and uncertainty included in the results used by decision makers and policy makers in industry and government. The emphasis is on models described by PDEs and their numerical solution. Procedures and results from all aspects of verification and validation are integrated with modern methods in uncertainty quantification and stochastic simulation. Methods for combining numerical approximation errors, uncertainty in model input parameters, and model form uncertainty are presented in order to estimate the uncertain response of a system in the presence of stochastic inputs and lack of knowledge uncertainty. This new edition has been extensively updated, including a fresh look at model accuracy assessment and the responsibilities of management for modeling and simulation activities. Extra homework problems and worked examples have been added to each chapter, suitable for course use or self-study. |
| Physical Description: | 1 Online-Ressource (xvii, 711 Seiten) |
| ISBN: | 9781009031004 |
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| spelling | Oberkampf, William L. 1944- Verification and validation in scientific computing Verification, validation, and uncertainty quantification in scientific computing William L. Oberkampf, Christopher J. Roy Second edition. Cambridge, United Kingdom ; New York, NY, USA Cambridge University Press 2025 1 Online-Ressource (xvii, 711 Seiten) txt c cr Can you trust results from modeling and simulation? This text provides a framework for assessing the reliability of and uncertainty included in the results used by decision makers and policy makers in industry and government. The emphasis is on models described by PDEs and their numerical solution. Procedures and results from all aspects of verification and validation are integrated with modern methods in uncertainty quantification and stochastic simulation. Methods for combining numerical approximation errors, uncertainty in model input parameters, and model form uncertainty are presented in order to estimate the uncertain response of a system in the presence of stochastic inputs and lack of knowledge uncertainty. This new edition has been extensively updated, including a fresh look at model accuracy assessment and the responsibilities of management for modeling and simulation activities. Extra homework problems and worked examples have been added to each chapter, suitable for course use or self-study. Roy, Christopher J. Erscheint auch als Druck-Ausgabe 9781316516133 |
| spellingShingle | Oberkampf, William L. 1944- Verification, validation, and uncertainty quantification in scientific computing |
| title | Verification, validation, and uncertainty quantification in scientific computing |
| title_alt | Verification and validation in scientific computing |
| title_auth | Verification, validation, and uncertainty quantification in scientific computing |
| title_exact_search | Verification, validation, and uncertainty quantification in scientific computing |
| title_full | Verification, validation, and uncertainty quantification in scientific computing William L. Oberkampf, Christopher J. Roy |
| title_fullStr | Verification, validation, and uncertainty quantification in scientific computing William L. Oberkampf, Christopher J. Roy |
| title_full_unstemmed | Verification, validation, and uncertainty quantification in scientific computing William L. Oberkampf, Christopher J. Roy |
| title_short | Verification, validation, and uncertainty quantification in scientific computing |
| title_sort | verification validation and uncertainty quantification in scientific computing |
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