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Unpublished Preprints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. Y. Morita, S. Rezaeiravesh, N. Tabatabaei, R. Vinuesa, K. Fukagata, and P. Schlatter,
    "Applying Bayesian optimization with Gaussian process regression to computational fluid dynamics problems,"
    arXiv preprint, arXiv:2101.09985 [physics.flu-dyn].

  2. M. Morimoto, K. Fukami, K. Zhang, A. G. Nair, and K. Fukagata,
    "Convolutional neural networks for fluid flow analysis: toward effective metamodeling and low-dimensionalization,"
    arXiv preprint, arXiv:2101.02535 [physics.flu-dyn].

  3. K. Fukami, R. Maulik, N. Ramachandra, K. Fukagata, and K. Taira,
    "Global field reconstruction from sparse sensors with Voronoi tessellation-assisted deep learning,"
    arXiv preprint, arXiv:2101.00554 [physics.flu-dyn].

  4. M. Morimoto, K. Fukami, K. Zhang, and K. Fukagata,
    "Generalization techniques of neural networks for fluid flow estimation,"
    arXiv preprint, arXiv:2011.11911 [physics.flu-dyn].

    • Sample code for Grad-CAM: Available on GitHub
  5. K. Fukami, K. Hasegawa, T. Nakamura, M. Morimoto, and K. Fukagata,
    "Model order reduction with neural networks: Application to laminar and turbulent flows,"
    arXiv preprint, arXiv:2011.10277 [physics.flu-dyn].

  6. K. Fukami, T. Murata, and K. Fukagata,
    "Sparse identification of nonlinear dynamics with low-dimensionalized flow representations,"
    arXiv preprint, arXiv:2010.12177 [physics.flu-dyn].

  7. 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].

Journal Articles

  1. T. Nakamura, K. Fukami, K. Hasegawa, Y. Nabae, and K. Fukagata,
    "Convolutional neural network and long short-term memory based reduced order surrogate for minimal turbulent channel flow,"
    Phys. Fluids 33, 025116 (2021).
    (Preprint, arXiv:2010.13351 [physics.flu-dyn]).

    • Sample code: Available on GitHub
  2. K. Fukami, K. Fukagata, and K. Taira,
    "Machine-learning-based spatio-temporal super resolution reconstruction of turbulent flows,"
    J. Fluid Mech. 909, A9 (2021).

  3. K. Hasegawa, K. Fukami, T. Murata, and K. Fukagata,
    "CNN-LSTM based reduced order modeling of two-dimensional unsteady flows around a circular cylinder at different Reynolds numbers,"
    Fluid Dyn. Res. 52, 065501 (2020).

  4. R. Maulik, K. Fukami, N. Ramachandra, K. Fukagata, and K. Taira,
    "Probabilistic neural networks for fluid flow surrogate modeling and data recovery,"
    Phys. Rev. Fluids 5, 104401 (2020).

  5. K. Fukami, T. Nakamura, and K. Fukagata,
    "Convolutional neural network based hierarchical autoencoder for nonlinear mode decomposition of fluid field data,"
    Phys. Fluids 32, 095110 (2020).
  6. 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. 34, 367-383 (2020).

    • Sample code: Available on GitHub
  7. K. Fukami, K. Fukagata, and K. Taira,
    "Assessment of supervised machine learning methods for fluid flows,"
    Theor. Comput. Fluid Dyn. 34, 497519 (2020).

  8. 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.
  9. K. Fukami, Y. Nabae, K. Kawai, and K. Fukagata,
    "Synthetic turbulent inflow generator using machine learning,"
    Phys. Rev. Fluids 4, 064603 (2019).

  10. 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)

  11. 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: 2021-02-26 (Fri) 02:24:41 (3d)
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