Data from: A probabilistic metric for the validation of computational models

A new validation metric is proposed that combines the use of a threshold
based on the uncertainty in the measurement data with a normalised
relative error, and that is robust in the presence of large variations in
the data. The outcome from the metric is the probability that a
model's predictions are representative of the real world based on the
specific conditions and confidence level pertaining to the experiment from
which the measurements were acquired. Relative error metrics are
traditionally designed for use with series of data values but orthogonal
decomposition has been employed to reduce the dimensionality of data
matrices to feature vectors so that the metric can be applied to fields of
data. Three previously published case studies are employed to demonstrate
the efficacy of this quantitative approach to the validation process in
the discipline of structural analysis, for which historical data was
available; however, the concept could be applied to a wide range of
disciplines and sectors where modelling and simulation plays a pivotal
role.