Uncertainty quantification in autoencoders predictions: Applications in aerodynamics

A data-driven model is compared to classical equation-driven approaches to investigate its ability to predict quantity of interest and their uncertainty when studying airfoil aerodynamics. The focus is on autoencoders and the effect of uncertainties due to the architecture, the hyperparamaters and t...

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Vydané v:	Journal of computational physics Ročník 506; s. 112951
Hlavní autori:	Saetta, Ettore, Tognaccini, Renato, Iaccarino, Gianluca
Médium:	Journal Article
Jazyk:	English
Vydavateľské údaje:	Elsevier Inc 01.06.2024
Predmet:	Airfoil aerodynamics Autoencoder Machine learning Uncertainty quantification Airfoil aerodynamics Uncertainty quantification Autoencoder Machine learning
ISSN:	0021-9991, 1090-2716
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