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Summary: Mixed Aleatory/Epistemic Uncertainty Quantification
for Hypersonic Flows via Gradient-Based Optimization
and Surrogate Models
Brian A. Lockwood
,
University of Wyoming, Laramie, WY, 82071, USA
Mihai Anitescu
,
Argonne National Laboratory, Argonne, IL, 60439, USA
and
Dimitri J. Mavriplis§
University of Wyoming, Laramie, WY, 82071, USA
The use of optimization for the propagation of mixed epistemic/aleatory uncertainties
is demonstrated within the context of hypersonic flows. Specifically, this work focuses
on strategies applicable for models where input parameters can be divided into a set of
variables containing only aleatory uncertainties and a set with epistemic uncertainties.
With the input parameters divided in this way, uncertainty due to the epistemic vari-
ables is propagated via a constrained optimization approach, while the uncertainty due
to aleatory variables is propagated via sampling. A statistics-of-intervals approach is pro-
posed in which the constrained optimization results are treated as a random variable and
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