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.

中文翻译：

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研究水动力不稳定性的完整CVDL方法

在流体动力学中，最重要的研究领域之一是流体动力学不稳定性及其在不同流态下的演变。对所述不稳定性的研究涉及高度非线性动力学。目前，三种主要方法用于理解这种现象——即分析模型、实验和模拟——并且所有这些方法都主要使用人类专业知识进行研究和关联。在这项工作中，我们声称并证明了这项研究工作的主要部分可以而且应该使用深度学习计算机视觉（CVDL，或深度计算机视觉）领域的最新突破性进展进行分析。具体来说，我们针对和评估特定的最先进技术——例如图像检索、模板匹配、参数回归和时空预测 - 因为它们提供了定量和定性的好处。为此，我们将研究重点放在最具代表性的不稳定性之一，即 Rayleigh-Taylor 不稳定性上，模拟其行为并创建一个开源的最先进的注释数据库 (RayleAI)。最后，我们使用调整后的实验结果和新颖的物理损失方法来验证预测结果与实际物理现实的对应关系，以证明模型的效率。在这项工作中开发和证明的技术可以作为流体动力学领域物理学家研究各种物理系统的重要工具，也可以通过迁移学习用于其他不稳定性研究。部分技术可以很容易地应用于已经存在的仿真结果。所有模型以及为这项工作创建的数据集都可在以下网址公开获取：https://github.com/scientific-computing-nrcn/SimulAI。