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Super-resolution is an innovative technique that upscales the resolution ofan image or a video and thus enables us to reconstruct high-fidelity imagesfrom low-resolution data. This study performs super-resolution analysis onturbulent flow fields spatially and temporally using various state-of-the-artmachine learning techniques like ESPCN, ESRGAN and TecoGAN to reconstructhigh-resolution flow fields from low-resolution flow field data, especiallykeeping in mind the need for low resource consumption and rapid resultsproduction/verification. The dataset used for this study is extracted from the'isotropic 1024 coarse' dataset which is a part of Johns Hopkins TurbulenceDatabases (JHTDB). We have utilized pre-trained models and fine tuned them toour needs, so as to minimize the computational resources and the time requiredfor the implementation of the super-resolution models. The advantages presentedby this method far exceed the expectations and the outcomes of regular singlestructure models. The results obtained through these models are then comparedusing MSE, PSNR, SAM, VIF and SCC metrics in order to evaluate the upscaledresults, find the balance between computational power and output quality, andthen identify the most accurate and efficient model for spatial and temporalsuper-resolution of turbulent flow fields.

A comparative study of various Deep Learning techniques for spatio-temporal Super-Resolution reconstruction of Forced Isotropic Turbulent flows