  Unsteady laminar vortex shedding over a circular cylinder is predicted using
a deep learning technique, a generative adversarial network (GAN), with a
particular emphasis on elucidating the potential of learning the solution of
the Navier-Stokes equations. Numerical simulations at two different Reynolds
numbers with different time-step sizes are conducted to produce training
datasets of flow field variables. Unsteady flow fields in the future at a
Reynolds number which is not in the training datasets are predicted using a
GAN. Predicted flow fields are found to qualitatively and quantitatively agree
well with flow fields calculated by numerical simulations. The present study
suggests that a deep learning technique can be utilized for prediction of
laminar wake flow in lieu of solving the Navier-Stokes equations.
