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Title: Efficiency of Uncertainty Propagation Methods for Estimating Output Moments
Uncertainty propagation methods are used to estimate the distribution of model outputs resulting from a set of uncertain model outputs. There are a number of uncertainty propagation methods available in literature. This paper compares six non-intrusive uncertainty propagation methods, Latin Hypercube Sampling, Full Factorial Integration, Univariate Dimension Reduction, Halton series, Sobol series, and Polynomial Chaos Expansion, in terms of their efficiency for estimating the first four moments of the output distribution using computational experiments. The results suggest employing FFNI if there are few uncertain inputs, up to three. Uncertainty propagation methods that utilize Halton and Sobol series are found to be robust for estimating output moments as the number of uncertain inputs increased. In general, higher order polynomial chaos expansion approximations (3rd-5th order) obtained accurate estimates of model outputs with fewer model evaluations.  more » « less
Award ID(s):
1743445
PAR ID:
10187866
Author(s) / Creator(s):
;
Date Published:
Journal Name:
Computeraided Chemical Engineering
Volume:
47
ISSN:
1570-7946
Page Range / eLocation ID:
487 - 493
Format(s):
Medium: X
Sponsoring Org:
National Science Foundation
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