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


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


Team

Publications

Unpublished Preprints

  1. K. Fukami, T. Nakamura, and K. Fukagata,
    "Convolutional neural network based hierarchical autoencoder for nonlinear mode decomposition of fluid field data,"
    arXiv preprint, arXiv:2006.06977 [physics.comp-ph].
  2. R. Maulik, K. Fukami, N. Ramachandra, K. Fukagata, and K. Taira,
    "Probabilistic neural networks for fluid flow model-order reduction and data recovery,"
    arXiv preprint, arXiv:2005.04271 [physics.flu-dyn].

  3. M. Morimoto, K. Fukami, and K. Fukagata,
    "Experimental velocity data estimation for imperfect particle images using machine learning,"
    arXiv preprint, arXiv:2005.00756 [physics.flu-dyn].

  4. K. Fukami, K. Fukagata, and K. Taira,
    "Machine learning based spatio-temporal super resolution reconstruction of turbulent flows,"
    arXiv preprint, arXiv:2004.11566 [physics.flu-dyn].

Journal Articles

  1. K. Hasegawa, K. Fukami, T. Murata, and K. Fukagata,
    "Machine-learning-based reduced order modeling for unsteady flows around bluff bodies of various shapes,"
    Theor. Comput. Fluid Dyn. (2020). https://doi.org/10.1007/s00162-020-00528-w
    (Preprint: arXiv:2003.07548 [physics.flu-dyn]).

  2. K. Fukami, K. Fukagata, and K. Taira,
    "Assessment of supervised machine learning methods for fluid flows,"
    Theor. Comput. Fluid Dyn. (2020). https://doi.org/10.1007/s00162-020-00518-y
    (Preprint: arXiv:2001.09618 [physics.flu-dyn])

  3. 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.
  4. K. Fukami, Y. Nabae, K. Kawai, and K. Fukagata,
    "Synthetic turbulent inflow generator using machine learning,"
    Phys. Rev. Fluids 4, 064603 (2019).

  5. Q. Wang, Y. Hasegawa, and T. Zaki,
    "Spatial reconstruction of steady scalar sources from remote measurements in turbulent flow,"
    J. Fluid Mech. 870, 316-352 (2019)

  6. 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).

Links


Last-modified: 2020-06-16 (Tue) 03:32:40 (18d)
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