Aleatory and Epistemic Uncertainty Quantification for Engineering Applications

Most computer models for engineering applications are developed to help assess a design or regulatory requirement. As part of this task, the capability to quantify the impact of variability and uncertainty in the decision context is critical. The requirement is often stated as: the probability that some system response quantity exceeds a threshold value is less than some required probability. This presentation will provide an outline and comparison of methods that are used for analyzing and propagating aleatory and epistemic uncertainties. The methods are all available in a software tool called DAKOTA. We will specifically focus on five classes of methods: Latin Hypercube sampling, analytic reliability methods, polynomial chaos expansions, Dempster-Shafer theory of evidence, and “second-order” probability analysis. Examples of each of the methods as applied to a simple engineering model will be provided