Assessing the reliability of complex models :: mathematical and statistical foundations of verification, validation, and uncertainty quantification /
"Advances in computing hardware and algorithms have dramatically improved the ability to simulate complex processes computationally. Today's simulation capabilities offer the prospect of addressing questions that in the past could be addressed only by resource-intensive experimentation, if...
Gespeichert in:
| Körperschaften: | , , |
|---|---|
| Format: | Elektronisch E-Book |
| Sprache: | English |
| Veröffentlicht: |
Washington, D.C. :
National Academies Press,
©2012.
|
| Schlagworte: | |
| Online-Zugang: | Volltext |
| Zusammenfassung: | "Advances in computing hardware and algorithms have dramatically improved the ability to simulate complex processes computationally. Today's simulation capabilities offer the prospect of addressing questions that in the past could be addressed only by resource-intensive experimentation, if at all. Assessing the Reliability of Complex Models recognizes the ubiquity of uncertainty in computational estimates of reality and the necessity for its quantification. As computational science and engineering have matured, the process of quantifying or bounding uncertainties in a computational estimate of a physical quality of interest has evolved into a small set of interdependent tasks: verification, validation, and uncertainty of quantification (VVUQ). In recognition of the increasing importance of computational simulation and the increasing need to assess uncertainties in computational results, the National Research Council was asked to study the mathematical foundations of VVUQ and to recommend steps that will ultimately lead to improved processes. [This report] discusses changes in education of professionals and dissemination of information that should enhance the ability of future VVUQ practitioners to improve and properly apply VVUQ methodologies to difficult problems, enhance the ability of VVUQ customers to understand VVUQ results and use them to make informed decisions, and enhance the ability of all VVUQ stakeholders to communicate with each other. This report is an essential resource for all decision and policy makers in the field, students, stakeholders, UQ experts, and VVUQ educators and practitioners"--Publisher's description. |
| Beschreibung: | 1 online resource (xi, 131 pages) : illustrations (some color) |
| Bibliographie: | Includes bibliographical references. |
| ISBN: | 9780309256353 0309256356 1283636220 9781283636223 6613948683 9786613948687 |
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| 520 | |a "Advances in computing hardware and algorithms have dramatically improved the ability to simulate complex processes computationally. Today's simulation capabilities offer the prospect of addressing questions that in the past could be addressed only by resource-intensive experimentation, if at all. Assessing the Reliability of Complex Models recognizes the ubiquity of uncertainty in computational estimates of reality and the necessity for its quantification. As computational science and engineering have matured, the process of quantifying or bounding uncertainties in a computational estimate of a physical quality of interest has evolved into a small set of interdependent tasks: verification, validation, and uncertainty of quantification (VVUQ). In recognition of the increasing importance of computational simulation and the increasing need to assess uncertainties in computational results, the National Research Council was asked to study the mathematical foundations of VVUQ and to recommend steps that will ultimately lead to improved processes. [This report] discusses changes in education of professionals and dissemination of information that should enhance the ability of future VVUQ practitioners to improve and properly apply VVUQ methodologies to difficult problems, enhance the ability of VVUQ customers to understand VVUQ results and use them to make informed decisions, and enhance the ability of all VVUQ stakeholders to communicate with each other. This report is an essential resource for all decision and policy makers in the field, students, stakeholders, UQ experts, and VVUQ educators and practitioners"--Publisher's description. | ||
| 504 | |a Includes bibliographical references. | ||
| 505 | 0 | |a Introduction -- Sources of uncertainty and error -- Verification -- Emulation, reduced-order modeling, and forward propagation -- Model validation and prediction -- Making decisions -- Next steps in practice, research, and education for verification, validation, and uncertainty quantification -- Appendixes. | |
| 588 | 0 | |a Online resource; title from title screen (National Academies Press, viewed July 6, 2012). | |
| 650 | 0 | |a Computer simulation. |0 http://id.loc.gov/authorities/subjects/sh85029533 | |
| 650 | 0 | |a Uncertainty |x Mathematical models. |0 http://id.loc.gov/authorities/subjects/sh2008113073 | |
| 650 | 0 | |a Digital computer simulation. |0 http://id.loc.gov/authorities/subjects/sh85037973 | |
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| 710 | 2 | |a National Research Council (U.S.). |b Board on Mathematical Sciences and Their Applications. |0 http://id.loc.gov/authorities/names/n2002014842 | |
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| 776 | 0 | 8 | |i Print version: |t Assessing the reliability of complex models. |d Washington : National Academies Press, 2012 |z 0309256348 |w (OCoLC)784035096 |
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| author_corporate | National Research Council (U.S.). Committee on Mathematical Foundations of Verification, Validation, and Uncertainty Quantification National Research Council (U.S.). Board on Mathematical Sciences and Their Applications National Research Council (U.S.). Division on Engineering and Physical Sciences |
| author_corporate_role | |
| author_facet | National Research Council (U.S.). Committee on Mathematical Foundations of Verification, Validation, and Uncertainty Quantification National Research Council (U.S.). Board on Mathematical Sciences and Their Applications National Research Council (U.S.). Division on Engineering and Physical Sciences |
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| contents | Introduction -- Sources of uncertainty and error -- Verification -- Emulation, reduced-order modeling, and forward propagation -- Model validation and prediction -- Making decisions -- Next steps in practice, research, and education for verification, validation, and uncertainty quantification -- Appendixes. |
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| indexdate | 2024-11-27T13:24:49Z |
| institution | BVB |
| institution_GND | http://id.loc.gov/authorities/names/no2012107530 http://id.loc.gov/authorities/names/n2002014842 http://id.loc.gov/authorities/names/n2002018789 |
| isbn | 9780309256353 0309256356 1283636220 9781283636223 6613948683 9786613948687 |
| language | English |
| lccn | 2013397241 |
| oclc_num | 798361535 |
| open_access_boolean | |
| owner | MAIN DE-863 DE-BY-FWS |
| owner_facet | MAIN DE-863 DE-BY-FWS |
| physical | 1 online resource (xi, 131 pages) : illustrations (some color) |
| psigel | ZDB-4-EBA |
| publishDate | 2012 |
| publishDateSearch | 2012 |
| publishDateSort | 2012 |
| publisher | National Academies Press, |
| record_format | marc |
| spelling | Assessing the reliability of complex models : mathematical and statistical foundations of verification, validation, and uncertainty quantification / Committee on Mathematical Foundations of Verification, Validation, and Uncertainty Quantification ; Board on Mathematical Sciences and Their Applications ; Division on Engineering and Physical Sciences. Washington, D.C. : National Academies Press, ©2012. 1 online resource (xi, 131 pages) : illustrations (some color) text txt rdacontent computer c rdamedia online resource cr rdacarrier data file rda "Advances in computing hardware and algorithms have dramatically improved the ability to simulate complex processes computationally. Today's simulation capabilities offer the prospect of addressing questions that in the past could be addressed only by resource-intensive experimentation, if at all. Assessing the Reliability of Complex Models recognizes the ubiquity of uncertainty in computational estimates of reality and the necessity for its quantification. As computational science and engineering have matured, the process of quantifying or bounding uncertainties in a computational estimate of a physical quality of interest has evolved into a small set of interdependent tasks: verification, validation, and uncertainty of quantification (VVUQ). In recognition of the increasing importance of computational simulation and the increasing need to assess uncertainties in computational results, the National Research Council was asked to study the mathematical foundations of VVUQ and to recommend steps that will ultimately lead to improved processes. [This report] discusses changes in education of professionals and dissemination of information that should enhance the ability of future VVUQ practitioners to improve and properly apply VVUQ methodologies to difficult problems, enhance the ability of VVUQ customers to understand VVUQ results and use them to make informed decisions, and enhance the ability of all VVUQ stakeholders to communicate with each other. This report is an essential resource for all decision and policy makers in the field, students, stakeholders, UQ experts, and VVUQ educators and practitioners"--Publisher's description. Includes bibliographical references. Introduction -- Sources of uncertainty and error -- Verification -- Emulation, reduced-order modeling, and forward propagation -- Model validation and prediction -- Making decisions -- Next steps in practice, research, and education for verification, validation, and uncertainty quantification -- Appendixes. Online resource; title from title screen (National Academies Press, viewed July 6, 2012). Computer simulation. http://id.loc.gov/authorities/subjects/sh85029533 Uncertainty Mathematical models. http://id.loc.gov/authorities/subjects/sh2008113073 Digital computer simulation. http://id.loc.gov/authorities/subjects/sh85037973 Simulation par ordinateur. simulation. aat COMPUTERS Computer Simulation. bisacsh Digital computer simulation fast Computer simulation fast Uncertainty Mathematical models fast National Research Council (U.S.). Committee on Mathematical Foundations of Verification, Validation, and Uncertainty Quantification. http://id.loc.gov/authorities/names/no2012107530 National Research Council (U.S.). Board on Mathematical Sciences and Their Applications. http://id.loc.gov/authorities/names/n2002014842 National Research Council (U.S.). Division on Engineering and Physical Sciences. http://id.loc.gov/authorities/names/n2002018789 has work: Assessing the Reliability of Complex Models (Text) https://id.oclc.org/worldcat/entity/E39PCYRw3C94XqRPVjYt7xMF8C https://id.oclc.org/worldcat/ontology/hasWork Print version: Assessing the reliability of complex models. Washington : National Academies Press, 2012 0309256348 (OCoLC)784035096 FWS01 ZDB-4-EBA FWS_PDA_EBA https://search.ebscohost.com/login.aspx?direct=true&scope=site&db=nlebk&AN=476038 Volltext |
| spellingShingle | Assessing the reliability of complex models : mathematical and statistical foundations of verification, validation, and uncertainty quantification / Introduction -- Sources of uncertainty and error -- Verification -- Emulation, reduced-order modeling, and forward propagation -- Model validation and prediction -- Making decisions -- Next steps in practice, research, and education for verification, validation, and uncertainty quantification -- Appendixes. Computer simulation. http://id.loc.gov/authorities/subjects/sh85029533 Uncertainty Mathematical models. http://id.loc.gov/authorities/subjects/sh2008113073 Digital computer simulation. http://id.loc.gov/authorities/subjects/sh85037973 Simulation par ordinateur. simulation. aat COMPUTERS Computer Simulation. bisacsh Digital computer simulation fast Computer simulation fast Uncertainty Mathematical models fast |
| subject_GND | http://id.loc.gov/authorities/subjects/sh85029533 http://id.loc.gov/authorities/subjects/sh2008113073 http://id.loc.gov/authorities/subjects/sh85037973 |
| title | Assessing the reliability of complex models : mathematical and statistical foundations of verification, validation, and uncertainty quantification / |
| title_auth | Assessing the reliability of complex models : mathematical and statistical foundations of verification, validation, and uncertainty quantification / |
| title_exact_search | Assessing the reliability of complex models : mathematical and statistical foundations of verification, validation, and uncertainty quantification / |
| title_full | Assessing the reliability of complex models : mathematical and statistical foundations of verification, validation, and uncertainty quantification / Committee on Mathematical Foundations of Verification, Validation, and Uncertainty Quantification ; Board on Mathematical Sciences and Their Applications ; Division on Engineering and Physical Sciences. |
| title_fullStr | Assessing the reliability of complex models : mathematical and statistical foundations of verification, validation, and uncertainty quantification / Committee on Mathematical Foundations of Verification, Validation, and Uncertainty Quantification ; Board on Mathematical Sciences and Their Applications ; Division on Engineering and Physical Sciences. |
| title_full_unstemmed | Assessing the reliability of complex models : mathematical and statistical foundations of verification, validation, and uncertainty quantification / Committee on Mathematical Foundations of Verification, Validation, and Uncertainty Quantification ; Board on Mathematical Sciences and Their Applications ; Division on Engineering and Physical Sciences. |
| title_short | Assessing the reliability of complex models : |
| title_sort | assessing the reliability of complex models mathematical and statistical foundations of verification validation and uncertainty quantification |
| title_sub | mathematical and statistical foundations of verification, validation, and uncertainty quantification / |
| topic | Computer simulation. http://id.loc.gov/authorities/subjects/sh85029533 Uncertainty Mathematical models. http://id.loc.gov/authorities/subjects/sh2008113073 Digital computer simulation. http://id.loc.gov/authorities/subjects/sh85037973 Simulation par ordinateur. simulation. aat COMPUTERS Computer Simulation. bisacsh Digital computer simulation fast Computer simulation fast Uncertainty Mathematical models fast |
| topic_facet | Computer simulation. Uncertainty Mathematical models. Digital computer simulation. Simulation par ordinateur. simulation. COMPUTERS Computer Simulation. Digital computer simulation Computer simulation Uncertainty Mathematical models |
| url | https://search.ebscohost.com/login.aspx?direct=true&scope=site&db=nlebk&AN=476038 |
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