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English version is here.

(2021ǯ)

  1. ظ 콤Ǥϳء׻ϳ콤
  2. ظʰѰ
  3. ز 鸡ƤѰ ѰĹ
  4. ز 踡ƤѰ Ѱ
  5. ز ήϳطϾѰ ѰĹ
  6. ز ֵ¸ ô
  7. β꡼ǥ󥰥ץ֥ХĶƥ꡼ץ ץô

س򿦡2021ǯ١ˡ

  1. Flow, Turbulence and Combustion (Springer), Editor
  2. 12th International Symposium on Turbulence and Shear Flow Phenomena (TSFP12), Secretary General
  3. JSTʣήư͢ݤβͽ¬˸ήβʳء ΰ襢ɥХ
  4. ܵز LAJѰ Ѱ
  5. ܵز ؽѻԽ Fluids Engineering ƥޥͥ㡼
  6. ܵز бĴѰ 󲽤ΤWG Ѱ
  7. ܵز ׻ϳصѼԻǧѰ Ǯήϳʬ 纺
  8. ܵز ǯ OS֥ץ饺ޥ奨 ʥ
  9. ܵز ǯ OSֵؽ✕ؤκü ʥ
  10. ܵز ήι A-TS 05-24֥ץ饺ޥ奨
  11. ήϳز ǯ OSAIήϳء ʥ
  12. ήϳزήϳإϥɥ֥å3ǡ33ϡή׼纺
  13. ήϳز ǯ2021¹԰Ѱ Ѱ
  14. ήϳز 36ήϳإݥ ¹԰ѰĹ

ܾ

  • 硧ήϳءή桿ή
  • ذ̡ΡʹءˡˡTeknDǥΩ

  • 1989ǯ3Ωع ´
  • 1994ǯ3 ƥ̻ҹز ´
  • 1997ǯ6ǥΩ(KTH)ر LicentiateλTeknL
  • 2000ǯ4ǥΩ(KTH)ر βλTeknD
  • 2000ǯ9 رطϸ ƥ̻ҹ칶 βλ, ()

  • 2000ǯ10̾Ⱦʹȵѱ Ѹ Ūͻ縦̸
  • 2001ǯ4ȵ縦 ͥ륮Ѹ 裱и
  • 2003ǯ4 رطϸ ʵ칶
  • 2007ǯ4 Ǥֻ ʵزʡ
  • 2011ǯ4 ڶ ʵزʡ
  • 2015ǯ4 ʵزʡ

ô (2021ǯ)

  1. ¸3ǯQ3, Q4ô
  2. ԥ塼ߥ졼αѡ3ǯQ3)
  3. ήϳء3ǯQ3
  4. ´ȸ4ǯ
  5. ήδäȿرաѸ
  6. ϳء׻ϳ裱رա
  7. ϳء׻ϳ裲رѸ
  8. 긦ʽ1ǯ
  9. ̸裱ʽ2ǯ
  10. Ķ󥷥ƥ๽ˡGESLա
  11. 絬ϴĶƥ๽ˡGESL
  12. ֥᥸㡼GESLա
  13. ̸裲Ρ

ֵ̹ʤ

  • ⹻Ƶθ (⹻3ǯ), 2010ǯ-
  • Ωع ˬֵ2018ǯ6
  • 븩Ωع ϵֵʹ⹻2ǯ, 2013ǯ10, 2014ǯ10
  • ʣƥΥǥηϡر(G-COE)ա, 2012ǯ6
  • SFCʳֺ (SFC⹻1ǯ), 2008ǯ32009ǯ3
  • Ķ񸻡ͥ륮ʳ裱 (ر), 2007ǯ4

ǶʸʸꥹȤGoogle Scholar Profile

  1. 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. (to appear).
    (Preprint, arXiv:2101.00554 [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,"
    Theor. Comput. Fluid Dyn. (to appear).
    (Preprint, arXiv:2101.02535 [physics.flu-dyn]).

    • Sample code: Available on GitHub
  3. M. Badri Ghomizad, H. Kor, and K. Fukagata,
    "A structured adaptive mesh refinement strategy with a sharp interface direct-forcing immersed boundary method for moving boundary problems,"
    J. Fluid Sci. Technol. 16, JFST0014 (2021).

  4. M. Badri Ghomizad, H. Kor, and K. Fukagata,
    "A sharp interface direct-forcing immersed boundary method using the moving least square approximation,"
    J. Fluid Sci. Technol. 16, JFST0013 (2021).

  5. M. Ohashi, K. Fukagata, and N. Tokugawa,
    "Adjoint-based sensitivity analysis for airfoil flow control aiming at lift-to-drag ratio improvement,"
    AIAA J. (2021). https://doi.org/10.2514/1.J060415

  6. 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).

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

  8. 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).

  9. 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).

  10. 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).
  11. S. Hirokawa, M. Ohashi, K. Eto, K. Fukagata, and N. Tokugawa,
    "Turbulent friction drag reduction on Clark-Y airfoil by passive uniform blowing,"
    AIAA J. 58, 4178-4180 (2020).

  12. R. Uekusa, A. Kawagoe, Y. Nabae, and K. Fukagata,
    "Resolvent analysis of turbulent channel flow with manipulated mean velocity profile,"
    J. Fluid Sci. Technol. 15, JFST0014 (2020).

  13. 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).

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

  15. S. Hirokawa, K. Eto, K. Fukagata, and N. Tokugawa,
    "Experimental investigation on friction drag reduction on an airfoil by passive blowing,"
    J. Fluid Sci. Technol. 15, JFST0011 (2020).

  16. M. Ohashi, Y. Morita, S. Hirokawa, K. Fukagata, and N. Tokugawa,
    "Parametric study toward optimization of blowing and suction locations for improving lift-to-drag ratio on a Clark-Y airfoil,"
    J. Fluid Sci. Technol. 15, JFST0008 (2020).

  17. Y. Nabae, K. Kawai, and K. Fukagata,
    "Prediction of drag reduction effect by streamwise traveling wave-like wall deformation in turbulent channel flow at practically high Reynolds numbers,"
    Int. J. Heat Fluid Flow 82, 108550 (2020).

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

ޤʤ

Ϣ

  • ϡ223-8522 ͻԹ̶3-14-1 ز
  • E-mail: fukagata [huh]mech.keio.ac.jp

Last-modified: 2021-07-22 () 13:05:19 (12d)
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