Prediction and Control of 2D Decaying Turbulence using Generative Adversarial Networks

In the current study, dynamics prediction of freely decaying 2D turbulence has been performed based on generative adversarial networks (GANs). Our prediction model, PredictionNet, showed high accuracy up to one integral time scale where the autocorrelation drops to around 0.25 with a correlation coefficient of 0.855. It also showed much higher accuracy on the enstrophy spectrum than the baseline convolutional neural network (CNN) model. By checking small-scale statistics and performing scale decomposition to investigate and quantify such differences in the predictive accuracy, we found that the GAN model can reflect the statistical properties and small-scale features of turbulence very well. In addition, as an example of applications, we used our PredictionNet as a surrogate model for the task of flow control. The control model, ControlNet, was capable of finding optimum disturbances that change the time-evolution of the flow field to a direction that fits an objective function, such as maximizing the propagation of the control effect. Although it is the prediction and control of relatively simple 2D turbulence, the current study results provide a new approach to the dynamics prediction and flow control that can be applied to more complex turbulence.