Assessing the Reliability of Complex Models; Mathematical and Statistical Foundations of Verification, Validation, and Uncertainty Quantification

National Research Council, Division of Engineering and Physical Sciences, Board on Mathematical Sciences and Their Applications, Committee on Mathematical Foundations of Verification, Validation, and Uncertainty Quantification

ISBN 10: 0309256348 / ISBN 13: 9780309256346

Editore: The National Academies Press, Washington DC, 2012

Riguardo questo articolo

xi, [1], 131, [1] pages. Footnotes. Formulae. Figures (some with color). Boxes (some with color). References. In recognition of the increasing importance of computational simulation and the increasing need to assess uncertainties in computational results, the National Research Council (NRC) was asked to study the mathematical foundations of VVUQ and to recommend steps that will ultimately lead to improved processes. The specific tasking to the Committee on Mathematical Foundations of Verification, Validation, and Uncertainty Quantification is as follows: ? A committee of the National Research Council will examine practices for VVUQ of large-scale computational simulations in several research communities. ? The committee will identify common concepts, terms, approaches, tools, and best practices of VVUQ. ? The committee will identify mathematical sciences research needed to establish a foundation for building a science of verification and validation (V&V) and for improving the practice of VVUQ. ? The committee will recommend educational changes needed in the mathematical sciences community and mathematical sciences education needed by other scientific communities to most effectively use VVUQ. 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. Assessing the Reliability of Complex Models 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.

Codice articolo 79972

Dati bibliografici

• Titolo: Assessing the Reliability of Complex Models; Mathematical and Statistical Foundations of Verification, Validation, and Uncertainty Quantification
• Editore: The National Academies Press, Washington DC
• Data di pubblicazione: 2012
• Lingua: Inglese
• ISBN 10: 0309256348
• ISBN 13: 9780309256346
• Rilegatura: Trade paperback
• Edizione: Presumed First Edition, First printing.
• Condizione: Very good

Descrizione articolo

Riassunto

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. Assessing the Reliability of Complex Models 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.