Model Predictive Capability Assessment Under Uncertainty

*† This paper develops methods to address important decision-making issues in the use of model-based simulation for reliability analysis and design. When computational models are developed, the assumptions and approximations introduce various types of errors in the code predictions. In order to accept the model prediction with confidence, the computational models need to be rigorously verified and validated. While model prediction has uncertainty, validation experiments also have measurement errors. Thus model validation involves comparing predictions with test data when both are uncertain. A validation metric based on Bayesian hypothesis testing is presented and the method is extended to consider multiple response quantities or a single model response at different spatial and temporal points. Another decision in the use of model-based prediction is to extrapolate the validation inference in the tested region to an inference about the predictive capability in the untested region of actual application, and to establish a certain confidence in the extrapolation being performed. A methodology is developed to propagate inferences across domains using Bayesian networks. The proposed methods are illustrated for application to structural dynamics problems.