  Recent attempts to use deep learning for super-resolution reconstruction of
turbulent flows have used supervised learning, which requires paired data for
training. This limitation hinders more practical applications of
super-resolution reconstruction. Therefore, we present an unsupervised learning
model that adopts a cycle-consistent generative adversarial network that can be
trained with unpaired turbulence data for super-resolution reconstruction. Our
model is validated using three examples: (i) recovering the original flow field
from filtered data using direct numerical simulation (DNS) of homogeneous
isotropic turbulence; (ii) reconstructing full-resolution fields using
partially measured data from the DNS of turbulent channel flows; and (iii)
generating a DNS-resolution flow field from large eddy simulation (LES) data
for turbulent channel flows. In examples (i) and (ii), for which paired data
are available for supervised learning, our unsupervised model demonstrates
qualitatively and quantitatively similar performance as that of the best
supervised-learning model. More importantly, in example (iii), where supervised
learning is impossible, our model successfully reconstructs the high-resolution
flow field of statistical DNS quality from the LES data. This demonstrates that
unsupervised learning of turbulence data is indeed possible, opening a new door
for the wide application of super-resolution reconstruction of turbulent
fields.
