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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Vydané v:	Computers & fluids Ročník 305; s. 106890
Hlavní autori:	Yu, Linqi, Chen, Yanyun, Yousif, Mustafa Z., Lim, Hee-Chang
Médium:	Journal Article
Jazyk:	English
Vydavateľské údaje:	Elsevier Ltd 30.01.2026
Predmet:	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
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