JSPS KAKENHI Grant-in-Aid for Scientific Research (A) (FY2018-2020, No. 18H03758)

Construction of feature extraction method for turbulence big data by machine learning


K. Fukami, Y. Nabae, K. Kawai, and K. Fukagata, Phys. Rev. Fluids 4, 064603 (2019).
Copyright © 2019 by the American Physical Society.



Journal Articles


  1. T. Murata, K. Fukami, and K. Fukagata,
    "Nonlinear mode decomposition with convolutional neural networks for fluid dynamics,"
    J. Fluid Mech. 882, A13 (2020).

    • Sample code: fileMD-CNN-AE.py
    • Sample flow field data is automatically downloaded. The details are noted in this code.
  2. K. Fukami, Y. Nabae, K. Kawai, and K. Fukagata,
    "Synthetic turbulent inflow generator using machine learning,"
    Phys. Rev. Fluids 4, 064603 (2019).

  3. K. Fukami, K. Fukagata, and K. Taira,
    "Super-resolution reconstruction of turbulent flows with machine learning,"
    J. Fluid Mech. 870, 106-120 (2019).

Conference Proceedings

  1. R. Yamaguchi, A. Mitsuishi, T. Shimura, K. Iwamoto, and A. Murata,
    "Prediction of time evolution of vortex structure in pulsating turbulent pipe flow by deep learning,"
    The 29th International Symposium on Transport Phenomena, Honolulu, Hawaii, USA (2018).


Last-modified: 2020-01-10 (Fri) 10:26:39 (86d)
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