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 inherently uncertain, leading to uncertainty in model outputs. Characterizing, propagating, and analy...
| Main Authors: | , |
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| Format: | Article |
| Language: | en_US |
| Published: |
American Institute of Aeronautics and Astronautics
2011
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| Online Access: | http://hdl.handle.net/1721.1/61706 https://orcid.org/0000-0003-2156-9338 |
| Summary: | 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 inherently
uncertain, leading to uncertainty in model outputs. Characterizing, propagating, and analyzing this uncertainty is
critical 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 Carlo
sampling) present an intractable computational burden. This paper presents a novel surrogate modeling
methodology designed specifically for propagating uncertainty from model inputs to model outputs and for
performing a global sensitivity analysis, which characterizes the contributions of uncertainties in model inputs to
output variance, while maintaining the quantitative rigor of the analysis by providing confidence intervals on
surrogate predictions. The approach is developed for a general class of models and is demonstrated on an aircraft
emissions 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 tradeoff
between computation time and uncertainty in the estimation of the statistical outputs of interest. |
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