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English version is here.

(2022ǯ)

  1. ظ 콤Ǥϳء׻ϳ콤
  2. ظʰѰ
  3. 祻󥿡 Ѱ
  4. ز 踡ƤѰ Ѱ
  5. ز ήϳطϾѰ ѰĹ
  6. ز ֵ¸ ô

س򿦡2022ǯ١ˡ

  1. Flow, Turbulence and Combustion (Springer), Editor
  2. 12th International Symposium on Turbulence and Shear Flow Phenomena (TSFP12), Secretary General
  3. 15th World Congress on Computational Mechanics (WCCM-APCOM2022), Local Executive Committee Member
  4. JSTʣήư͢ݤβͽ¬˸ήβʳء ΰ襢ɥХ
  5. ܳؽѲ ϳʬʲ IUTAMϢȾѰ Ѱ
  6. ܵز LAJѰ Ѱ
  7. ܵز ؽѻԽ Fluids Engineering ƥޥͥ㡼
  8. ܵز бĴѰ 󲽤ΤWG Ѱ
  9. ܵز ׻ϳصѼԻǧѰ Ǯήϳʬ ꡼
  10. ܵز ǯ OSֵؽ✕ؤκü ʥ
  11. ܵز RC286ήŪ¬ߥ졼ˡήξιѤ˴ؤ븦ʬʲ Ѱ
  12. ܵز ήι A-TS 05-24֥ץ饺ޥ奨
  13. ܵز ήι P-SCD410֤ή¿ͤʵǽõüʳصѤؤαѤ˴ؤ븦ʬʲ(3) Ѱ
  14. ܵز ׻ϳ ָήôѰ ѰĹ
  15. ήϳز
  16. ήϳز ǯ OSAIήϳء ʥ
  17. ήϳزήϳإϥɥ֥å3ǡ33ϡή׼纺
  18. 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 (2022ǯ)

  1. ¸3ǯô
  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

ֵ̹ʤ

  • ⹻Ƶθ (⹻3ǯ), 2010-2019ǯ
  • Ωع ˬֵ2018ǯ6
  • 븩Ωع ϵֵʹ⹻2ǯ, 2013ǯ10, 2014ǯ10
  • ʣƥΥǥηϡر(G-COE)ա, 2012ǯ6
  • SFCʳֺ (SFC⹻1ǯ), 2008ǯ32009ǯ3
  • Ķ񸻡ͥ륮ʳ裱 (ر), 2007ǯ4

ǶνʪʸꥹȤGoogle Scholar Profile

  1. 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. (2022). https://doi.org/10.1007/s10494-022-00334-w

  2. T. Nakamura and K. Fukagata,
    "Robust training approach of neural networks for fluid flow state estimations,"
    Int. J. Heat Fluid Flow 96, 108977 (2022).

  3. D. Hiruma, R. Onishi, K. Takahashi, and K. Fukagata,
    "Sensitivity study on storm modulation through a strategic use of consumer air conditioners,"
    Atmos. Sci. Lett. (2022). http://doi.org/10.1002/asl.1091

  4. S. Miura, M. Ohashi, K. Fukagata, and N. Tokugawa,
    "Drag reduction by uniform blowing on the pressure surface of an airfoil,"
    AIAA J. 60, 2241-2250 (2022).

  5. 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).

  6. K. Fukagata,
    News & Views: "Towards quantum computing of turbulence,"
    Nat. Comput. Sci. 2, 68-69 (2022).

  7. 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).

  8. 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).

ޤʤ

Ϣ

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

ޤ


Last-modified: 2022-06-22 () 16:30:13 (6d)
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