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Synopsis
Discover how verification of codes and simulations, validation of models, allied with uncertainty quantification, improves decision making.
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About the Authors
William L. Oberkampf has more than fifty years of experience in research and development in fluid dynamics, heat transfer, and solid mechanics. Over the past twnety-five years he has focused on research and teaching of verification, validation, and uncertainty quantification in modeling and simulation. He is a Fellow of AIAA and a Fellow of NAFEMS.
Christopher J. Roy is Professor in the Kevin T. Crofton Department of Aerospace and Ocean Engineering at Virginia Tech. He has worked primarily in the area of computational fluid dynamics, but has participated in or organized multiple validation experiments. He has taught more than fifty short courses in the field of verification, validation, and uncertainty quantification and has more than 200 publications in the field.
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- PublisherCambridge University Press
- Publication date2025
- ISBN 10 131651613X
- ISBN 13 9781316516133
- BindingHardcover
- LanguageEnglish
- Edition number2
- Number of pages729
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Verification, Validation, and Uncertainty Quantification in Scientific Computing
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Verification, Validation, and Uncertainty Quantification in Scientific Computing
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Verification, Validation, and Uncertainty Quantification in Scientific Computing
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Verification, Validation, and Uncertainty Quantification in Scientific Computing
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Verification, Validation, and Uncertainty Quantification in Scientific Computing (Hardcover)
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Hardcover. Condition: new. Hardcover. 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. This updated edition provides a framework for assessing the reliability and uncertainty of modeling and simulation results used in industry and government. With extra worked examples and homework problems for graduate students, and new material on topics including model accuracy assessment and management responsibilities for modeling activities. Shipping may be from our UK warehouse or from our Australian or US warehouses, depending on stock availability. Seller Inventory # 9781316516133
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Verification, Validation, and Uncertainty Quantification in Scientific Computing
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Verification, Validation, and Uncertainty Quantification in Scientific Computing
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Verification, Validation, and Uncertainty Quantification in Scientific Computing
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Verification, Validation, and Uncertainty Quantification in Scientific Computing
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