  This study proposes a novel deep-learning-based method for generating reduced
representations of turbulent flows that ensures efficient storage and transfer
while maintaining high accuracy during decompression. A Swin-Transformer
network combined with a physical constraints-based loss function is utilized to
compress the turbulent flows with high compression ratios and then restore the
data with the underlying physical properties. The forced isotropic turbulent
flow is used to demonstrate the ability of the Swin-Transformer-based (ST)
model, where the instantaneous and statistical results show the excellent
ability of the model to recover the flow data with remarkable accuracy.
Furthermore, the capability of the ST model is compared with a typical
Convolutional Neural Network-based auto-encoder (CNN-AE) by using the turbulent
channel flow at two friction Reynolds numbers $Re_\tau$ = 180 and 550. The
results generated by the ST model are significantly more consistent with the
DNS data than those recovered by the CNN-AE, indicating the superior ability of
the ST model to compress and restore the turbulent flow. This study also
compares the compression performance of the ST model at different compression
ratios (CR) and finds that the model has low enough error even at very high CR.
Additionally, the effect of transfer learning (TL) is investigated, showing
that TL reduces the training time by 64\% while maintaining high accuracy. The
results illustrate for the first time that the Swin-Transformer-based model
incorporating a physically constrained loss function can compress and restore
turbulent flows with the correct physics.
