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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.Read more
- Authors are internationally recognized contributors and experts in the field
- Includes material suitable for undergraduate students with limited experience in the numerical solution of partial differential equations
- Provides detailed worked examples to aid independent study
Reviews & endorsements
"This ambitious and well-written book is an excellent comprehensive review of what you need to know to evaluate or build a complex model or simulation of a physical process."
Joan Horvath, Computing Reviews
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- Date Published: November 2010
- format: Hardback
- isbn: 9780521113601
- length: 790 pages
- dimensions: 246 x 175 x 46 mm
- weight: 1.45kg
- contains: 220 b/w illus. 14 colour illus. 55 tables
- availability: Available
Table of Contents
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
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