In this paper, a series of algorithms are proposed to address the problems in the NASA Langley Research Center Multidisciplinary Uncertainty Quantification Challenge. A Bayesian approach is employed to characterize and calibrate the epistemic parameters based on the available data, whereas a variance-based global sensitivity analysis is used to rank the epistemic and aleatory model parameters. A nested sampling of the aleatory–epistemic space is proposed to propagate uncertainties from model parameters to output quantities of interest.
Probabilistic Methods for Sensitivity Analysis and Calibration in the NASA Challenge Problem
Journal of Aerospace Information Systems ; 12 , 1 ; 219-234
2014-09-10
16 pages
Article (Journal)
Electronic Resource
English
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