Model validation based on probability boxes under mixed uncertainties

Abstract

A non-probabilistic validation framework based on probability boxes (p-boxes) is developed for estimating predictive uncertainty involving input parameter, model form, and experimental uncertainties. These uncertainties are treated as aleatory and epistemic uncertainties. In order to characterize model form uncertainty, an interval-valued area validation metric is introduced to characterize the disagreement between the quantitative predictions from simulation model and experimental data when either or both of predictions and experimental data are represented by probability boxes, whose scales are the same as the original physical units. When experimental data belong to different distributions or physical observations, an extended u-pooling method is used to pool all the comparisons at different sites together, and it offers a global metric for model validation and is interval-valued too. The proposed method is examined by the thermal challenge problem from the Sandia Validation Challenge Workshop.

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