VVUQ plays an important role, with far-reaching consequences, in making sense of the information provided by computational models, observations, and expert judgment. It is important to communicate the best practices of VVUQ to those creating and using computational models and also to instructors in university programs.
To this end, several activities could be undertaken. For example, to provide assistance to instructors, some model problems and solutions have to be made available. In this spirit, people with expertise in the areas of VVUQ can be encouraged to write an article or series of articles targeted to an educational journal, in which problems are introduced and solutions are outlined.
Recommendation: Federal agencies should promote the dissemination of VVUQ materials and the offering of informative events for instructors and practitioners.
This type of contribution would go a long way toward sharing important ideas and suggesting how they might be implemented in a classroom setting. Along the same lines, the NAE could perhaps devote a special issue of its quarterly publication The Bridge to this type of initiative. It is also important to build on existing resources, such as the American Statistical Association’s Guidelines for Assessment and Instruction in Statistics Education, which addresses the statistical component of VVUQ and highlights the need for a good understanding of data modeling, data analysis, data interpretation, and decisions. For existing practitioners, educational activities should be routinely included at conferences and also through the mathematical sciences institutes (e.g., the Statistical and Applied Mathematical Sciences Institute in Research Triangle Park, North Carolina, and the Mathematical Sciences Research Institute in Berkeley, California).
This chapter attempts to peer into the future of VVUQ and to summarize the committee’s responses to its tasking. It identifies key principles that we found to be helpful and identifies best practices that the committee has observed in the application of VVUQ to difficult problems in computational science and engineering. It identifies research areas that promise to improve the mathematical foundations that undergird VVUQ processes. Finally, it discusses changes in the education of professionals and dissemination of information that should enhance the ability of future VVUQ practitioners to improve and properly apply VVUQ methodologies to difficult problems, enhance the ability of VVUQ customers to understand VVUQ results and use them to make informed decisions, and enhance the ability of all VVUQ stakeholders to communicate with each other. These observations and recommendations are offered in the hope that they will help the VVUQ community as it continues to improve VVUQ processes and broaden their applications.
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