  Simulations of complex turbulent flow are part and parcel of the engineering
design process. Eddy viscosity based turbulence models represent the workhorse
for these simulations. The underlying simplifications in eddy viscosity models
make them computationally inexpensive but also introduce structural
uncertainties in their predictions. Currently the Eigenspace Perturbation
Method is the only approach to predict these uncertainties. Due to its purely
physics based nature this method often leads to unrealistically large
uncertainty bounds that lead to exceedingly conservative designs. We use a Deep
Learning based approach to address this issue. We control the perturbations
using trained deep learning models that predict how much to perturb the modeled
Reynolds stresses. This is executed using a Convolutional Neural Network that
learns the difference between eddy viscosity based model predictions and high
fidelity data as a mapping of flow features. We show that this approach leads
to improvements over the Eigenspace Perturbation Method.
