Using Statistical Computer Experiments as Part of a Math Model Validation Process

Improved computational models and simulations offer the potential to reduce design, development and manufacturing time and cost by providing better predictions, and a more complete exploration of system and process design space. However, these models and simulations will not achieve this potential unless credible uncertainty statements about their predictions accompany them. Key to attaining this goal is a model validation process that permits comparison of computational predictions to experimental outcomes over a meaningful set of validation experiments—a process that will provide confidence in the predictive capability of the computational model. The ultimate goal is to design, conduct and analyze validation experiments so as to be able to credibly make statements such as: “Based on our analysis of the validation experiments, we are 80% confident that if we measured an actual system, it would fall within these bands about the output of the model we just ran”. In this paper, a general validation strategy will be proposed along with some working definitions. Both field and computer experiments play a prominent role. An academic example problem will be presented to illustrate the procedure.