  Engineering design and scientific analysis rely upon computer simulations of
turbulent fluid flows using turbulence models. These turbulence models are
empirical and approximate, leading to large uncertainties in their predictions
that hamper scientific and engineering advances. We outline a Physics
Constrained Deep Learning framework to estimate turbulence model uncertainties
using physics based Eigenspace Perturbations along with Deep Learning based
guidance. The Deep Learning based modulation controls the spatial variation in
perturbation magnitude to improve the calibration of uncertainty estimates over
the state of the art physics based methods.
