Verification, Validation, and Uncertainty Quantification in Scientific Computing

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Verification, Validation, and Uncertainty Quantification in Scientific Computing by William L. Oberkampf, Christopher J. Roy

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.

FORMAT
Hardcover CONDITION
Brand New Publisher Description

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.

Table of Contents

1. Introduction; Part I. Fundamental Concepts: 2. Fundamental concepts and terminology; 3. Modeling and computational simulation; Part II. Code Verification and Software Engineering: 4. Software engineering for scientific computing; 5. Code order-of-accuracy verification; 6. Exact and manufactured solutions; Part III. Solution Verification: 7. Numerical uncertainty estimation; 8. Iterative error; 9. Discretization error; Part IV. Model Validation and Predictive Capability: 10. Model validation fundamentals; 11. Design and execution of model validation experiments; 12. Model accuracy assessment; 13. Predictive capability; Part V. Planning, Management, and Implementation Issues: 14. Planning and prioritization in modeling and simulation; 15. Maturity assessment of modeling and simulation; 16. Verification, validation, and uncertainty quantification responsibilities and management; Index.

Review

'This landmark book, by two top-level experts in VVUQ, is a major contribution to the field of scientific computing. It provides the concepts and the tools to develop critical judgement when evaluating results from numerical simulations. Addressing students, researchers as well as practitioners. An essential and necessary contribution towards gaining confidence in the computational based design process!' Charles Hirsch, Vrije Universiteit Brussel

Promotional

Discover how verification of codes and simulations, validation of models, allied with uncertainty quantification, improves decision making.

Details ISBN131651613X Author Christopher J. Roy Publisher Cambridge University Press Edition Description 2nd Revised edition Edition 2nd ISBN-13 9781316516133 Format Hardcover Imprint Cambridge University Press Place of Publication Cambridge Country of Publication United Kingdom Illustrations Worked examples or Exercises DEWEY 502.85 Year 2025 Alternative 9781009031004 ISBN-10 131651613X Audience Further / Higher Education Pages 729 Publication Date 2025-04-03 UK Release Date 2025-04-03 Country of Origin US Product Class Description Science & Mathematics: Textbooks & Study Guides
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