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Complete Deep Computer-Vision Methodology for Investigating Hydrodynamic Instabilities

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High Performance Computing (ISC High Performance 2020)

Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 12321))

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Abstract

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 highly non-linear dynamics. Currently, three main methods are used for understanding of such phenomena – namely analytical and statistical models, experiments, and simulations – and all of them are primarily investigated and correlated using human expertise. This work demonstrates how 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, this work targets and evaluates 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, this research focuses mainly on one of the most representative instabilities, the Rayleigh-Taylor instability (RTI). We include an annotated database of images returned from simulations of RTI (RayleAI). Finally, adjusted experimental results and novel physical loss methodologies were used to validate the correspondence of the predicted results to actual physical reality to evaluate the model efficiency. The techniques which were developed and proved in this work can serve as essential tools for physicists in the field of hydrodynamics for investigating a variety of physical systems. Some of them can be easily applied on already existing simulation results, while others could be used via Transfer Learning to other instabilities research. All models as well as the dataset that was created for this work, are publicly available at: https://github.com/scientific-computing-nrcn/SimulAI.

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Acknowledgments

This work was supported by the Lynn and William Frankel Center for Computer Science. Computational support was provided by the NegevHPC project [56].

Author information

Authors and Affiliations

  1. Department of Physics, Bar-Ilan University, Ramat-Gan, Israel

    Re’em Harel

  2. Israel Atomic Energy Commission, P.O.B. 7061, Tel Aviv, Israel

    Re’em Harel, Matan Rusanovsky & Yehonatan Fridman

  3. Department of Computer Science, Ben-Gurion University of the Negev, P.O.B. 653, Be’er-Sheva, Israel

    Matan Rusanovsky, Yehonatan Fridman & Gal Oren

  4. Department of Physics, Nuclear Research Center-Negev, P.O.B. 9001, Be’er-Sheva, Israel

    Assaf Shimony & Gal Oren

Authors
  1. Re’em Harel
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  2. Matan Rusanovsky
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  3. Yehonatan Fridman
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  4. Assaf Shimony
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  5. Gal Oren
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Corresponding author

Correspondence to Gal Oren .

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Editors and Affiliations

  1. University of Tennessee at Knoxville, Knowville, TN, USA

    Heike Jagode

  2. Department of Mathematics, KIT für Technologie Karlsruhe, Karlsruhe, Baden-Württemberg, Germany

    Hartwig Anzt

  3. Computational Science, Helmholtz-Zentrum Dresden Rossendorf, Dresden, Sachsen, Germany

    Guido Juckeland

  4. Extreme Computing Research Center, King Abdullah University of Science and Technology, Thuwal, Saudi Arabia

    Hatem Ltaief

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Harel, R., Rusanovsky, M., Fridman, Y., Shimony, A., Oren, G. (2020). Complete Deep Computer-Vision Methodology for Investigating Hydrodynamic Instabilities. In: Jagode, H., Anzt, H., Juckeland, G., Ltaief, H. (eds) High Performance Computing. ISC High Performance 2020. Lecture Notes in Computer Science(), vol 12321. Springer, Cham. https://doi.org/10.1007/978-3-030-59851-8_5

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  • DOI: https://doi.org/10.1007/978-3-030-59851-8_5

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