  Within the domain of Computational Fluid Dynamics, Direct Numerical
Simulation (DNS) is used to obtain highly accurate numerical solutions for
fluid flows. However, this approach for numerically solving the Navier-Stokes
equations is extremely computationally expensive mostly due to the requirement
of greatly refined grids. Large Eddy Simulation (LES) presents a more
computationally efficient approach for solving fluid flows on lower-resolution
(LR) grids but results in an overall reduction in solution fidelity. Through
this paper, we introduce a novel deep learning framework SR-DNS Net, which aims
to mitigate this inherent trade-off between solution fidelity and computational
complexity by leveraging deep learning techniques used in image
super-resolution. Using our model, we wish to learn the mapping from a coarser
LR solution to a refined high-resolution (HR) DNS solution so as to eliminate
the need for performing DNS on highly refined grids. Our model efficiently
reconstructs the high-fidelity DNS data from the LES like low-resolution
solutions while yielding good reconstruction metrics. Thus our implementation
improves the solution accuracy of LR solutions while incurring only a marginal
increase in computational cost required for deploying the trained deep learning
model.
