Surrogate Modeling for Uncertainty Assessment with Application to Aviation Environmental System Models

Numerical simulation models to support decision-making and policy-making processes are often complex,involving many disciplines, many inputs, and long computation times. Inputs to such models are inherentlyuncertain, leading to uncertainty in model outputs. Characterizing, propagating, and analyzing this uncertainty iscritical both to model development and to the effective application of model results in a decision-making setting;however, the many thousands of model evaluations required to sample the uncertainty space (e.g., via Monte Carlosampling) present an intractable computational burden. This paper presents a novel surrogate modelingmethodology designed specifically for propagating uncertainty from model inputs to model outputs and forperforming a global sensitivity analysis, which characterizes the contributions of uncertainties in model inputs tooutput variance, while maintaining the quantitative rigor of the analysis by providing confidence intervals onsurrogate predictions. The approach is developed for a general class of models and is demonstrated on an aircraftemissions prediction model that is being developed and applied to support aviation environmental policy-making.The results demonstrate how the confidence intervals on surrogate predictions can be used to balance the tradeoffbetween computation time and uncertainty in the estimation of the statistical outputs of interest.