  In this paper, deep learning (DL) methods are evaluated in the context of
turbulent flows. Various generative adversarial networks (GANs) are discussed
with respect to their suitability for understanding and modeling turbulence.
Wasserstein GANs (WGANs) are then chosen to generate small-scale turbulence.
Highly resolved direct numerical simulation (DNS) turbulent data is used for
training the WGANs and the effect of network parameters, such as learning rate
and loss function, is studied. Qualitatively good agreement between DNS input
data and generated turbulent structures is shown. A quantitative statistical
assessment of the predicted turbulent fields is performed.
