Assessing the Reliability of Computational Models under Uncertainty

This paper investigates the use of the model reliability approach for validating computational models in the presence of both aleatory and epistemic uncertainty. The model reliability approach was originally developed to compare the mean of the model prediction against mean of the experimental data, in the presence of aleatory uncertainty. This paper extends this methods to (1) include both aleatory and epistemic uncertainty; and (2) account for the entire probability distributions of model prediction and experimental data. Two different types of data are considered for validation: 1) well-characterized data where output measurements and their corresponding input measurements are available, and (2) uncharacterized experiments where output measurements are alone available, and the uncertainty in the inputs is known. Different types of uncertainty – physical variability in the inputs and model parameters, data uncertainty in the form of sparse and interval data, and measurement errors in both the input and the output are included in the model validation procedure. The proposed methods are illustrated using a model which is intended to predict the energy dissipation in a lap joint under impact loading.