  Achievement of solutions in Navier-Stokes equation is one of challenging
quests, especially for its closure problem. For achievement of particular
solutions, there are variety of numerical simulations including Direct
Numerical Simulation (DNS) or Large Eddy Simulation (LES). These methods
analyze flow physics through efficient reduced-order modeling such as proper
orthogonal decomposition or Koopman method, showing prominent fidelity in fluid
dynamics. Generative adversarial network (GAN) is a reprint of neurons in brain
as combinations of linear operations, using competition between generator and
discriminator. Current paper propose deep learning network for prediction of
small-scale movements with large-scale inspections only, using GAN. Therefore
DNS result of three-dimensional mixing-layer was filtered blurring out the
small-scaled structures, then is predicted of its detailed structures,
utilizing Generative Adversarial Network (GAN). This enables multi-resolution
analysis being asked to predict fine-resolution solution with only inspection
of blurry one. Within the grid scale, current paper present deep learning
approach of modeling small scale features in turbulent flow. The presented
method is expected to have its novelty in utilization of unprocessed simulation
data, achievement of 3D structures in prediction by processing 3D convolutions,
and predicting precise solution with less computational costs.
