Predicting Critical Heat Flux with Uncertainty Quantification and Domain Generalization Using Conditional Variational Autoencoders and Deep Neural Networks

Deep generative models (DGMs) have proven to be powerful in generating realistic data samples. Their capability to learn the underlying distribution of a dataset enable them to generate synthetic data samples that closely resemble the original training dataset, thus addressing the challenge of data...

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Veröffentlicht in:	arXiv.org 2024-09
Hauptverfasser:	Alsafadi, Farah, Furlong, Aidan, Wu, Xu
Format:	Artikel
Sprache:	eng
Schlagworte:	Artificial neural networks Datasets Errors Heat flux Heat transfer Lookup tables Neural networks Performance prediction Regression models Synthetic data Uncertainty
Online-Zugang:	Volltext
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