ML  Project


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K. Fukami, K. Hasegawa, T. Nakamura, M. Morimoto, and K. Fukagata, SN Comput. Sci. 2, 467 (2021).


Team

Publications

Unpublished Preprints

  1. T. Nakamura and K. Fukagata,
    "Robust training approach of neural networks for fluid flow state estimations,"
    arXiv preprint, arXiv:2112.02751 [physics.flu-dyn].

  2. M. Morimoto, K. Fukami, R. Maulik, R. Vinuesa, and K. Fukagata,
    "Assessments of model-form uncertainty using Gaussian stochastic weight averaging for fluid-flow regression,"
    arXiv preprint, arXiv:2109.08248 [physics.flu-dyn].

  3. N. Moriya, K. Fukami, Y. Nabae, M. Morimoto, T. Nakamura, and K. Fukagata,
    "Inserting machine-learned virtual wall velocity for large-eddy simulation of turbulent channel flows,"
    arXiv preprint, arXiv:2106.09271 [physics.flu-dyn].

  4. T. Nakamura, K. Fukami, and K. Fukagata,
    "Comparison of linear regressions and neural networks for fluid flow problems assisted with error-curve analysis,"
    arXiv preprint, arXiv:2105.00913 [physics.flu-dyn].

  5. M. Matsuo, T. Nakamura, M. Morimoto, K. Fukami, and K. Fukagata,
    "Supervised convolutional network for three-dimensional fluid data reconstruction from sectional flow fields with adaptive super-resolution assistance,"
    arXiv preprint, arXiv:2103.09020 [physics.flu-dyn].

    • Sample code: Available on GitHub

Journal Articles

  1. Y. Nabae and K. Fukagata,
    "Bayesian optimization of traveling wave-like wall deformation for friction drag reduction in turbulent channel flow,"
    J. Fluid Sci. Technol. (to appear).

  2. 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,"
    J. Comput. Phys. 449 110788 (2022).

  3. M. Morimoto, K. Fukami, K. Zhang, and K. Fukagata,
    "Generalization techniques of neural networks for fluid flow estimation,"
    Neural Comput. Appl. (2021). https://doi.org/10.1007/s00521-021-06633-z

  4. K. Fukami, K. Hasegawa, T. Nakamura, M. Morimoto, and K. Fukagata,
    "Model order reduction with neural networks: Application to laminar and turbulent flows,"
    SN Comput. Sci. 2, 467 (2021).

  5. M. Morimoto, K. Fukami, and K. Fukagata,
    "Experimental velocity data estimation for imperfect particle images using machine learning,"
    Phys. Fluids 33, 087121 (2021). Editor's pick

    • Sample code: Available on GitHub
  6. K. Fukami, T. Murata, K. Zhang, and K. Fukagata,
    "Sparse identification of nonlinear dynamics with low-dimensionalized flow representations,"
    J. Fluid Mech. 926, A10 (2021).

    • Sample code: Available on GitHub
  7. K. Fukami, R. Maulik, N. Ramachandra, K. Fukagata, and K. Taira,
    "Global field reconstruction from sparse sensors with Voronoi tessellation-assisted deep learning,"
    Nat. Mach. Intell. 3, 945-951 (2021),

  8. 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,"
    Theor. Comput. Fluid Dyn. 35, 633-658 (2021).

    • Sample code: Available on GitHub
  9. W. Kobayashi, T. Shimura, A. Mitsuishi, K. Iwamoto, and A. Murata,
    "Prediction of the drag reduction effect of pulsating pipe flow based on machine learning,"
    Int. J. Heat Fluid Flow 88, 108783 (2021).

  10. 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). Editor's pick

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

  12. 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). Highlights of 2020

  13. 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).

  14. 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).
  15. 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
  16. K. Fukami, K. Fukagata, and K. Taira,
    "Assessment of supervised machine learning methods for fluid flows,"
    Theor. Comput. Fluid Dyn. 34, 497519 (2020).

  17. 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.
  18. K. Fukami, Y. Nabae, K. Kawai, and K. Fukagata,
    "Synthetic turbulent inflow generator using machine learning,"
    Phys. Rev. Fluids 4, 064603 (2019).

  19. 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)

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


Last-modified: 2021-12-07 (Tue) 03:20:20 (1d)
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