ML  Project†

• JSPS KAKENHI (S) (FY2021-2025, No. 21H05007)
  "Creation and implementation of an innovative flow control paradigm utilizing machine learning"
• JSPS KAKENHI (A) (FY2018-2020, No. 18H03758)
  "Construction of feature extraction method for turbulence big data by machine learning"
  The page for (A) only is here.

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

Team†

• Principal Investigator: Koji Fukagata (Keio Univ)
• Co-Investigators: Makoto Yamamoto (Tokyo Univ Sci), Yosuke Hasegawa (Univ Tokyo), Kaoru Iwamoto (Tokyo Univ Agri Technol), Takahiro Tsukahara (Tokyo Univ Sci), Naoya Fukushima (Tokai Univ)，Hiroya Mamori (Univ Electro-Commun)

Publications†

Review Articles†

1. K. Fukagata, K. Iwamoto, and Y. Hasegawa,
   "Turbulent drag reduction by streamwise traveling waves of wall-normal forcing,"
   Annu. Rev. Fluid Mech. 56, 69-90 (2024).

2. K. Fukagata,
   "Reduced order modeling of fluid flows using convolutional neural networks,"
   J. Fluid Sci. Technol. 18, JFST0002 (2023).

Unpublished Preprints†

1. T. Ishize, H. Omichi, and K. Fukagata,
   "Flow control by a hybrid use of machine learning and control theory,"
   arXiv preprint, arXiv:2311.08624 [physics.flu-dyn].

2. R. Miura and K. Fukagata,
   "Semi-supervised machine learning model for Lagrangian flow state estimation,"
   arXiv preprint, arXiv:2311.08754 [physics.flu-dyn].

3. H. Omichi, T. Ishize, and K. Fukagata,
   "Machine learning based dimension reduction for a stable modeling of periodic flow phenomena,"
   arXiv preprint, arXiv:2311.08765 [physics.flu-dyn].

4. 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].

Research Articles†

1. Y. Nabae and K. Fukagata,
   "Theoretical and numerical analyses of turbulent plane Couette flow controlled using uniform blowing and suction,"
   Int. J. Heat Fluid Flow 106, 109286 (2024).

2. M. Matsuo, T. Nakamura, M. Morimoto, K. Fukami, and K. Fukagata,
   "Reconstructing three-dimensional bluff body wake from sectional flow fields with convolutional neural networks,
   SN Comput. Sci. 5, 306 (2024).
   (Preprint, arXiv:2103.09020 [physics.flu-dyn])

3. K. Fukami, K. Fukagata, and K. Taira,
   "Super-resolution analysis via machine learning: A survey for fluid flows,"
   Theor. Comput. Fluid Dyn. 37, 421–444 (2023).
   (Preprint, arXiv:2301.10937 [physics.flu-dyn])

4. T. Sonoda, Z. Liu, T. Itoh, and Y. Hasegawa,
   "Reinforcement learning of control strategies for reducing skin friction drag in a fully developed turbulent channel flow,"
   J. Fluid Mech. 930, A30 (2023).

5. H. Wang and Y. Hasegawa,
   "Multi-objective optimization of actuation waveform for high-precision drop-on-demand inkjet printing,"
   Phys. Fluids 35, 013318 (2023).

6. M. Atzori, F. Mallor, R. Pozuelo, K. Fukagata, R. Vinuesa, and P. Schlatter,
   "A new perspective on skin-friction contributions in adverse-pressure-gradient turbulent boundary layers,"
   Int. J. Heat Fluid Flow 101, 109117 (2023).

7. T. Ishigami, M. Irikura, and T. Tsukahara,
   "Applicability of convolutional neural network for estimation of turbulent diffusion distance from source point,"
   Processes 10, 860 (2022).

8. T. Ishigami, M. Irikura, T. Tsukahara,
   "Machine learning to estimate the mass-diffusion distance from a point source under turbulent conditions,"
   Processes 10, 860 (2022).

9. M. Morimoto, K. Fukami, R. Maulik, R. Vinuesa, and K. Fukagata,
   "Assessments of epistemic uncertainty using Gaussian stochastic weight averaging for fluid-flow regression,"
   Physica D 440, 133454 (2022).
   (Preprint, arXiv:2109.08248 [physics.flu-dyn])

10. Y. Nabae and K. Fukagata,
    "Drag reduction effect of streamwise traveling wave-like wall deformation with spanwise displacement variation in turbulent channel flow,"
    Flow Turbul. Combust. 109, 1175–1194 (2022).

11. T. Nakamura and K. Fukagata,
    "Robust training approach of neural networks for fluid flow state estimations,"
    Int. J. Heat Fluid Flow 96, 108977 (2022).
    (Preprint, arXiv:2112.02751 [physics.flu-dyn])

12. T. Nakamura, K. Fukami, and K. Fukagata,
    "Identifying key differences between linear stochastic estimation and neural networks for fluid flow regressions,"
    Sci. Rep. 12, 3726 (2022).
    (Preprint: arXiv:2105.00913 [physics.flu-dyn]).

13. 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).
    

    • Preprint: arXiv:2101.09985 [physics.flu-dyn]

14. M. Morimoto, K. Fukami, K. Zhang, and K. Fukagata,
    "Generalization techniques of neural networks for fluid flow estimation,"
    Neural Comput. Appl. 34, 3647–3669 (2022).

    • Preprint: arXiv:2011.11911 [physics.flu-dyn]
    • Sample code for Grad-CAM: Available on GitHub

15. 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. 16, JFST0024 (2021).

16. 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).
    

17. 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

18. 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

19. 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),
    

    • Preprint: arXiv:2101.00554 [physics.flu-dyn]
    • Sample code: Available on GitHub

20. 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

21. 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).

22. 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

23. K. Fukami, K. Fukagata, and K. Taira,
    "Machine-learning-based spatio-temporal super resolution reconstruction of turbulent flows,"
    J. Fluid Mech. 909, A9 (2021).
    

24. 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

25. 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).
    

26. 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).
    
    • Sample code: HierarchicalAE_fig3.py

27. 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

28. K. Fukami, K. Fukagata, and K. Taira,
    "Assessment of supervised machine learning methods for fluid flows,"
    Theor. Comput. Fluid Dyn. 34, 497–519 (2020).

29. 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: MD-CNN-AE.py *Sample flow field data is automatically downloaded. The details are noted in this code.
    • Sample code with sample data are also available here.

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

    • Sample code: Available on GitHub
    • Animation: Machine-Learned Turbulence Generator, Ver. 2 (MLTG2).

31. 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)

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

    • Sample code: Available at UCLA Taira Lab.

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