Assessing the Reliability of Complex Models Mathematical and Statistical Foundations of Verification, Validation, and Uncertainty Quantification (2012) / Chapter Skim
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7 Next Steps in Practice, Research, and Education for Verification, Validation, and Uncertainty Quantification
7 Next Steps in Practice, Research, and Education for Verification, Validation, and Uncertainty Quantification
Pages 95-106
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From page 95... ...
As high-quality computational modeling becomes avail able in more application areas, the role played by VVUQ will continue to grow. Previous chapters have addressed VVUQ as it has evolved to date in the computational modeling of complex physical systems.
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From page 96... ...
Since VVUQ results are specific to particular QOIs in particular settings, transferring results to new QOIs and settings can be difficult to justify. However, one can consider applying VVUQ to a model over a broad set of conditions and QOIs if physi cal data are available to support such wide-ranging assessments of model accuracy and there is a firm theoretical understanding of the physical phenomena being modeled.
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From page 97... ...
It can be risky to assume that numerical errors in the reference problem are representative of numerical errors in the problem at hand. 7.1.2 Validation and Prediction Principles and Best Practices Although the questions involving solution verification are firmly grounded in mathematical and computational science, the questions that arise in validation and prediction require statistical and subject-matter (physics, chemis try, materials, etc.)
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From page 98... ...
• Principle: The efficiency and effectiveness of a validation assessment are often improved by exploiting the hierarchical composition of computational and mathematical models, with assessments beginning on the lowest-level building blocks and proceeding to successively more complex levels. -- Best practice: Identify hierarchies in computational and mathematical models, seek measured data that facilitate hierarchical validation assessments, and exploit the hierarchical composition to the extent possible.
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From page 99... ...
If this information is summarized and communicated properly, results from the VVUQ process can play a unique and significant role in the efficient allocation of resources, management of the overall uncertainty budget, and generation of the soundest possible basis for high-consequence decisions in the presence of uncertainties. The results of VVUQ analyses can also be used to make decisions regarding how to allocate resources for future VVUQ activities -- computing hardware acquisition, experimental campaigns, model improvement efforts, and other efforts -- to improve prediction accuracy or to improve confidence in model-based predictions.
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From page 100... ...
As is discussed in Chapter 3, methods exist for estimating tight two-sided bounds for numerical error in the solution of linear elliptic partial differential equations (PDEs) , but research is needed to develop a similar level of maturity for estimating error given more complicated mathematical models.
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From page 101... ...
7.3.2 UQ Research Although continued effort in improving methodology for building response surfaces and reduced-order models will likely prove fruitful in VVUQ, new research directions that consider VVUQ issues from a broader perspec tive are likely to yield more substantial gains in efficiency and accuracy. For example, response surface methods mentioned in Chapter 4 may consider both probabilistic descriptions of the input and the form of the mathematical/ computational model to describe output uncertainty, leading to efficiency gains over standard approaches.
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From page 102... ...
The preceding paragraphs discuss areas in which improvements are needed in validation and prediction meth odology, and more detail is provided in Chapter 5. Here the committee summarizes some research directions that have the potential to lead to significantly improved validation and prediction methods.
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From page 103... ...
For instance, it is unlikely that a policy maker will carry out the task of code verification, but it is important that the person making the decisions understand the difference between a code that has gone through a VVUQ process and a code that has not. Conversely, it is equally important that computational modelers are cognizant of the potential uses of the computer code and that the predictive limitations of the com putational model are clearly spelled out.
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From page 104... ...
VVUQ sits at the confluence of statistics, physics/engineering, and computing, which are themes that are usually discussed separately. To appreciate these distinctions, note that uncertainty is intimately associated with both observations and computational models.
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From page 105... ...
Finding: Interdisciplinary programs incorporating VVUQ methodology are emerging as a result of investment by granting bodies. Recommendation: Support for interdisciplinary programs in predictive science, including VVUQ, should be made available for education and training to produce personnel who are highly qualified in VVUQ methods.
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From page 106... ...
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 recom mendations 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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