  In fluid dynamics, one of the most important research fields is hydrodynamic
instabilities and their evolution in different flow regimes. The investigation
of said instabilities is concerned with the highly non-linear dynamics.
Currently, three main methods are used for understanding of such phenomenon -
namely analytical models, experiments and simulations - and all of them are
primarily investigated and correlated using human expertise. In this work we
claim and demonstrate that a major portion of this research effort could and
should be analysed using recent breakthrough advancements in the field of
Computer Vision with Deep Learning (CVDL, or Deep Computer-Vision).
Specifically, we target and evaluate specific state-of-the-art techniques -
such as Image Retrieval, Template Matching, Parameters Regression and
Spatiotemporal Prediction - for the quantitative and qualitative benefits they
provide. In order to do so we focus in this research on one of the most
representative instabilities, the Rayleigh-Taylor one, simulate its behaviour
and create an open-sourced state-of-the-art annotated database (RayleAI).
Finally, we use adjusted experimental results and novel physical loss
methodologies to validate the correspondence of the predicted results to actual
physical reality to prove the models efficiency. The techniques which were
developed and proved in this work can be served as essential tools for
physicists in the field of hydrodynamics for investigating a variety of
physical systems, and also could be used via Transfer Learning to other
instabilities research. A part of the techniques can be easily applied on
already exist simulation results. All models as well as the data-set that was
created for this work, are publicly available at:
https://github.com/scientific-computing-nrcn/SimulAI.
