Verification, Validation, and Uncertainty Quantification in Scientific Computing

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.

• Suitable as a text, or supplementary text, for undergraduate and graduate courses, with new homework problems and worked examples added to each chapter
• Includes material suitable for undergraduate students with limited experience in the numerical solution of partial differential equations
• Provides detailed examples in code verification, solution verification, model validation, uncertainty quantification, and predictive capability
• Includes three chapters on how organizations can plan, manage and implement credibility assessment procedures