Three-dimensional super-resolution reconstruction of turbulent flow using 3D-ESRGAN with random sampling strategy

This study introduces a deep learning framework that uses an enhanced three-dimensional super-resolution generative adversarial network (3D-ESRGAN) to reconstruct high-resolution turbulent flow fields from low-resolution data. To minimize the reliance on complete datasets during training, a random s...

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Bibliographic Details

Published in:	Computers & fluids Vol. 305; p. 106890
Main Authors:	Yu, Linqi, Chen, Yanyun, Yousif, Mustafa Z., Lim, Hee-Chang
Format:	Journal Article
Language:	English
Published:	Elsevier Ltd 30.01.2026
Subjects:	3D-ESRGAN Deep learning Random sampling strategy Super-resolution reconstruction Three-dimensional turbulent flow Deep learning Random sampling strategy Super-resolution reconstruction Three-dimensional turbulent flow 3D-ESRGAN
ISSN:	0045-7930
Online Access:	Get full text
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