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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Veröffentlicht in:	Journal of computational physics Jg. 506; S. 112951
Hauptverfasser:	Saetta, Ettore, Tognaccini, Renato, Iaccarino, Gianluca
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	Elsevier Inc 01.06.2024
Schlagworte:	Airfoil aerodynamics Autoencoder Machine learning Uncertainty quantification Airfoil aerodynamics Uncertainty quantification Autoencoder Machine learning
ISSN:	0021-9991, 1090-2716
Online-Zugang:	Volltext
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