  The rapidly advancing field of Fluid Mechanics has recently employed Deep
Learning to solve various problems within that field. In that same spirit we
try to perform Direct Numerical Simulation(DNS) which is one of the tasks in
Computational Fluid Dynamics, using three fundamental architectures in the
field of Deep Learning that were each used to solve various high dimensional
problems. We train these three models in an autoencoder manner, for this the
dataset is treated like sequential frames given to the model as input. We
observe that recently introduced architecture called Transformer significantly
outperforms its counterparts on the selected dataset.Furthermore, we conclude
that using Transformers for doing DNS in the field of CFD is an interesting
research area worth exploring.
