Verification and Validation in Scientific Computing

Can you trust results from modelling and simulation? Verification, validation, and uncertainty quantification can help.

William L. Oberkampf (Author), Christopher J. Roy (Author)

9780521113601, Cambridge University Press

Hardback, published 14 October 2010

790 pages, 220 b/w illus. 14 colour illus. 55 tables
25.4 x 17.9 x 4 cm, 1.68 kg

'This book provides a comprehensive and systematic development of basic concepts and procedures for verification and validation of models and simulations.' Zentralblatt MATH

Advances in scientific computing have made modelling and simulation an important part of the decision-making process in engineering, science, and public policy. This book provides a comprehensive and systematic development of the basic concepts, principles, and procedures for verification and validation of models and simulations. The emphasis is placed on models that are described by partial differential and integral equations and the simulations that result from their numerical solution. The methods described can be applied to a wide range of technical fields, from the physical sciences, engineering and technology and industry, through to environmental regulations and safety, product and plant safety, financial investing, and governmental regulations. This book will be genuinely welcomed by researchers, practitioners, and decision makers in a broad range of fields, who seek to improve the credibility and reliability of simulation results. It will also be appropriate either for university courses or for independent study.

Preface
1. Introduction
Part I. Fundamental Concepts: 2. Fundamental concepts and terminology
3. Modeling and computational simulation
Part II. Code Verification: 4. Software engineering
5. Code verification
6. Exact solutions
Part III. Solution Verification: 7. Solution verification
8. Discretization error
9. Solution adaptation
Part IV. Model Validation and Prediction: 10. Model validation fundamentals
11. Design and execution of 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. Development and responsibilities for verification, validation and uncertainty quantification
Appendix. Programming practices
Index.

Subject Areas: Maths for computer scientists [UYAM], Numerical analysis [PBKS], Differential calculus & equations [PBKJ]