An investigation on machine learning predictive accuracy improvement and uncertainty reduction using VAE-based data augmentation

The confluence of ultrafast computers with large memory, rapid progress in Machine Learning (ML) algorithms, and the availability of large datasets place multiple engineering fields at the threshold of dramatic progress. However, a unique challenge in nuclear engineering is data scarcity because exp...

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Veröffentlicht in:	Nuclear engineering and design Jg. 445; S. 114433
Hauptverfasser:	Alsafadi, Farah, Yaseen, Mahmoud, Wu, Xu
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	United States Elsevier B.V 15.12.2025 Elsevier BV
Schlagworte:	Bayesian neural network Conformal prediction Data augmentation Uncertainty quantification Variational autoencoders Variational autoencoders Uncertainty quantification Data augmentation Bayesian neural network Conformal prediction
ISSN:	0029-5493
Online-Zugang:	Volltext
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