  Turbulent problems in industrial applications are predominantly solved using
Reynolds Averaged Navier Stokes (RANS) turbulence models. The accuracy of the
RANS models is limited due to closure assumptions that induce uncertainty into
the RANS modeling. We propose the use of deep learning algorithms via
convolution neural networks along with data from direct numerical simulations
to extract the optimal set of features that explain the evolution of turbulent
flow statistics. Statistical tests are used to determine the correlation of
these features with the variation in the quantities of interest that are to be
predicted. These features are then used to develop improved partial
differential equations that can replace classical Reynolds Averaged Navier
Stokes models and show improvement in the accuracy of the predictions.
