Verification, Validation, and Uncertainty Quantification in Scientific Computing

William L. Oberkampf, Christopher J. Roy

ISBN 10: 131651613X / ISBN 13: 9781316516133

Editore: Cambridge University Press, GB, 2025

Riguardo questo articolo

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.

Codice articolo LU-9781316516133

Dati bibliografici

• Titolo: Verification, Validation, and Uncertainty Quantification in Scientific Computing
• Editore: Cambridge University Press, GB
• Data di pubblicazione: 2025
• Lingua: Inglese
• ISBN 10: 131651613X
• ISBN 13: 9781316516133
• Rilegatura: Hardback
• Edizione: seconda edizione
• Condizione: New
• Dimensioni: 17,78 L x 3,96 A x 25,4 P centimetri 17,78 centimetri di larghezza per 3,96 centimetri di altezza per 25,4 centimetri di profondità
• Peso: 1645 grammi

Descrizione articolo

Riassunto

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.