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From a mathematical perspective, validation is the process of assessing whether or not the quantity of interest (QOI) for a physical system is within some tolerancedetermined by the intended use of the modelof the model prediction.
Although prediction sometimes refers to situations where no data exist, in this report it refers to the output of the model in general.
In simple settings validation could be accomplished by directly comparing model results to physical measurements for the QOI and computing a confidence interval for the difference, or carrying out a hypothesis test of whether or not the difference is greater than the tolerance (see Oberkampf and Roy, 2010, Chapter 12).
In other settings, a more complicated statistical modeling formulation may be required to combine simulation output, various kinds of physical observations, and expert judgment to produce a prediction with accompanying prediction uncertainty, which can then be used for the assessment.
This more complicated formulation can also produce predictions for system behavior in new domains where no physical observations are available (see Bayarri et al., 2007a; Wang et al., 2009; or the case studies of this chapter).
Assessing prediction uncertainty is crucial for both validation (which involves comparison with measured data) and prediction of yet-unmeasured QOIs.
This uncertainty typically comes from a number of sources, including:
Although prediction sometimes refers to situations where no data exist, in this report it refers to the output of the model in general.
In simple settings validation could be accomplished by directly comparing model results to physical measurements for the QOI and computing a confidence interval for the difference, or carrying out a hypothesis test of whether or not the difference is greater than the tolerance (see Oberkampf and Roy, 2010, Chapter 12).
In other settings, a more complicated statistical modeling formulation may be required to combine simulation output, various kinds of physical observations, and expert judgment to produce a prediction with accompanying prediction uncertainty, which can then be used for the assessment.
This more complicated formulation can also produce predictions for system behavior in new domains where no physical observations are available (see Bayarri et al., 2007a; Wang et al., 2009; or the case studies of this chapter).
Assessing prediction uncertainty is crucial for both validation (which involves comparison with measured data) and prediction of yet-unmeasured QOIs.
This uncertainty typically comes from a number of sources, including:
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