  With the recent rapid developments in machine learning (ML), several attempts
have been made to apply ML methods to various fluid dynamics problems. However,
the feasibility of ML for predicting turbulence dynamics has not yet been
explored in detail. In this study, PredictionNet, a data-driven ML framework
based on generative adversarial networks (GANs), was developed to predict
two-dimensional (2D) decaying turbulence. The developed prediction model
accurately predicted turbulent fields at a finite lead time of up to half the
Eulerian integral time scale. In addition to the high accuracy in pointwise
metrics, various turbulence statistics, such as the probability density
function, spatial correlation function, and enstrophy spectrum, were accurately
captured by the employed GAN. Scale decomposition was used to interpret the
predictability depending on the spatial scale, and the role of latent variables
in the discriminator network was investigated. The good performance of the GAN
in predicting small-scale turbulence is attributed to the scale-selection
capability of the latent variable. Results also revealed that the recursive
applications of the prediction model yielded better predictions than single
predictions for large lead times. Furthermore, by utilizing PredictionNet as a
surrogate model, a control model named ControlNet was developed to identify
disturbance fields that drive the time evolution of the flow field in the
direction that optimises the specified objective function. Therefore, an
illustrative example in which the evolution of 2D turbulence can be predicted
within a finite time horizon and controlled using a GAN-based deep neural
network is presented.
