Verification and Validation in Scientific Computing

Abstract

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.

Unveiling WARIS Code, a Parallel and Multi-purpose FDM Framework

“…Meeting all those requirements lead to a successful project. In order to succeed in such a challenge, an application development life-cycle methodology must be applied [10]. Besides, life-cycle methodology is particularly important when HPC software comes into play.…”

Cross-platform validation of notional baseline architecture models of naval electric ship power systems

“…A number of guidelines and recommendations related to verification and validation have been proposed by various organizations and research communities such as the Society for Computer Simulation, the IEEE, the United States Department of Defense (DoD), the American Institute of Aeronautics and Astronautics (AIAA), and the American Society of Mechanical Engineers (ASME), a general summary of which is provided by [3]. However, [3] points out that, while there are important points to draw from each of these sources, there are different motivations and intended applications among them and a distinction and limitation of scope is made for verification and validation of scientific models.…”

“…However, [3] points out that, while there are important points to draw from each of these sources, there are different motivations and intended applications among them and a distinction and limitation of scope is made for verification and validation of scientific models. For example, while the IEEE definitions of verification and validation, which tend toward general software development activities, focus on adherence to defined requirements, validation of scientific models is concerned with assessing the degree to which a simulation model properly represents the physical system upon which it is based.…”

“…In this context of verification and validation of scientific models, the primary objectives are quantification of sources of error in the simulation, such as discretization and truncation error, quantification and propagation of uncertainty, and comparison with experimental results in the context of the known error and uncertainty. As it is noted in [3] that scientific models generally cannot be universally validated, the process of validation is an ongoing one, aimed at presenting a body of evidence attesting to the accuracy of the model in the context of which it has been evaluated.…”

Quantification of Incertitude in Black Box Simulation Codes

“…This issue is often neglected in studies in part because of the difficulty in addressing it. As with verification and validation, uncertainty quantification is critical to credible computational science, particularly in astrophysics where most simulation results are predictions, that is, a description of the state of a system under conditions for which the computational model has not been validated [1,2,3]. …”