--Overview--

3D-ESRGAN is developed to reconstruct 3D super-resolution channel flow from low-resolution data.

--Dependencies--

1. Python 3.6-3.8

2. tensorflow >=2.2.0<2.4.0 (cuDNN=7.6, CUDA=10.1 for tensorflow-gpu)

3. Numpy < 1.19

--Data preparation--

100 snapshots (channel flow at Reτ = 180) and its low-resolution data  are provided for tutorial. Before the input, the data should be normalized using code nor_fluc3d.py to get normalized data.

--Training--

Use 3d-esrgan.py to train the deep learning model. It will save architecture and weights files automatically.

--BibTex citation--

@article{doi:10.1063/5.0129203,

author = {Yu,Linqi and Yousif,Mustafa Z. and Zhang,Meng and Hoyas,Sergio and Vinuesa,Ricardo and Lim,Hee-Chang},

title = {Three-dimensional ESRGAN for super-resolution reconstruction of turbulent flows with tricubic interpolation-based transfer learning},

journal = {Physics of Fluids},

volume = {34},

number = {12},

pages = {125126},

year = {2022},

doi = {10.1063/5.0129203},

URL = { https://doi.org/10.1063/5.0129203},

eprint = { https://doi.org/10.1063/5.0129203}

}

--Note--

There is a typo in eq(4) and (5). The sigmoid function should be applied for the whole right side of the equation.