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

Benchmarking of Deep Learning models on 2D Laminar Flow behind Cylinder