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Calibration and validation metrics that involve decomposition of simulation and test data have been developed for potential use in the quantification of margin and uncertainty. The uniqueness of these validation metrics allows for nearly fullfield, simulation and test data over a wide range of spatial realizations (three-dimensional responses over multiple input conditions) and temporal (time or frequency) information, as needed. Currently, no other calibration/validation metrics have been developed that span multiple spatial realizations and temporal information simultaneously. A demonstration example utilizing two datasets explains how the calibration/validation metrics are formed and how they can be used to quantify the margin between the simulation and the test data as well as how it can quantify the uncertainty. The primary advantage of a proposed principal component analysis validation metric is that it preserves the engineering units of the original data so that the quantifications of margin and uncertainty can be made in engineering unit. A second advantage of the principal component analysis validation metric is that it can be used over a wide range of temporal information. The potential case of using sets of data with mismatched degree of freedom information is also explored. The general approach of using decomposition methods as the basis for calibration/validation metrics is extended to image decomposition methods. All decomposition methods successfully quantify margin and uncertainty in this general calibration/validation metric approach.

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