Probabilistic Assessment of CAE Models

This paper investigates a wide range of statistical methods for application in model validation under uncertainty. Hypothesis testing methods are explored first and an interval-based testing is found to be more practically useful for model validation than the commonly used point null hypothesis testing. Also, a more direct approach is proposed by formulating model validation as a reliability estimation problem. The proposed methods are illustrated and compared using numerical examples.