Uncertainty quantification in a mechanical submodel driven by a Wasserstein-GAN

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Veröffentlicht in:	IFAC-PapersOnLine 2022-01, Vol.55 (20), p.469-474
Hauptverfasser:	BOUKRAICHI, Hamza, AKKARI, Nissrine, CASENAVE, Fabien, RYCKELYNCK, David
Format:	Artikel
Sprache:	eng
Schlagworte:	Machine Learning Statistics
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doi_str_mv	10.1016/j.ifacol.2022.09.139
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source	Alma/SFX Local Collection
subjects	Machine Learning Statistics
title	Uncertainty quantification in a mechanical submodel driven by a Wasserstein-GAN
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