A Framework for Model Validation
- Sandia National Laboratories
Computational models have the potential of being used to make credible predictions in place of physical testing in many contexts, but success and acceptance require a convincing model validation. In general, model validation is understood to be a comparison of model predictions to experimental results but there appears to be no standard framework for conducting this comparison. This paper gives a statistical framework for the problem of model validation that is quite analogous to calibration, with the basic goal being to design and analyze a set of experiments to obtain information pertaining to the `limits of error' that can be associated with model predictions. Implementation, though, in the context of complex, high-dimensioned models, poses a considerable challenge for the development of appropriate statistical methods and for the interaction of statisticians with model developers and experimentalists. The proposed framework provides a vehicle for communication between modelers, experimentalists, and the analysts and decision-makers who must rely on model predictions.
- Research Organization:
- Sandia National Laboratories, Albuquerque, NM, and Livermore, CA
- Sponsoring Organization:
- USDOE
- DOE Contract Number:
- AC04-94AL85000
- OSTI ID:
- 3351
- Report Number(s):
- SAND99-0301C; ON: DE00003351
- Country of Publication:
- United States
- Language:
- English
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