Dynamic feature capturing in a fluid flow reduced-order model using attention-augmented autoencoders

This study looks into how adding adaptive attention to convolutional autoencoders can help reconstruct flow fields in fluid dynamics applications. The study compares the effectiveness of the proposed adaptive attention mechanism with the convolutional block attention module approach using two differ...

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Veröffentlicht in:	Engineering applications of artificial intelligence Jg. 149; S. 110463
Hauptverfasser:	Beiki, Alireza, Kamali, Reza
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	Elsevier Ltd 01.06.2025
Schlagworte:	Adaptive attention mechanisms Convolutional autoencoders Flow field reconstruction Reduced-order modeling Time series forecasting in fluid dynamics Convolutional autoencoders Reduced-order modeling Flow field reconstruction Time series forecasting in fluid dynamics Adaptive attention mechanisms
ISSN:	0952-1976
Online-Zugang:	Volltext
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