The verification and uncertainty quantification of surrogate models used for structural analysis.

High fidelity modeling of complex systems can require large finite element models to capture the physics of interest. Typically these high-order models take an excessively long time to run. For important studies such as model validation and uncertainty quantification, where probabilistic measures of the response are required, a large number of simulations of the high fidelity model with different parameters are necessary. In addition, some environments, such as an extensive random vibration excitation, require a long simulation time to capture the entire event. A process that produces a highly efficient model from the original high order model is necessary to enable these analyses. These highly efficient models are referred to as surrogate models, for their purpose is to represent the main physics that is of importance, but decrease the computational burden. A critical aspect of any surrogate model is how faithfully the efficient model represents the original high-order model. This paper describes the process for verifying a surrogate model using response quantities of interest and quantifying the introduced uncertainties in the use of the surrogate model. This is done to help any analyst if they use any surrogate model; such has, Craig-Bampton Reductions, POD, etc.