オ。ウ」ウリスャ、ホ・レ。シ・ク


fig_ae_small.png

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


クヲオ貭ホタゥ

クヲオ賣ワナェ

  • ホョ、、ホタゥク讀ヒオ。ウ」ウリスャ、ヘム、、、、ウ、ネ、ヒ、隍遙、ソキ、ソ、ハホョ、、ホタゥク貍ヒ。ケステロ、ホハヒ。マタ、トーニ、キ、ソ、、。・

シ遉ハクヲオ貘ョイフ

フ、スミネヌ、ホ・ラ・・ラ・・・ネ

  1. 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].

  2. 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].

  3. 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

クヲオ賺タハク

  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).
    (Preprint, arXiv:2112.02751 [physics.flu-dyn])

  3. 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]).

  4. 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).

  5. 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).

  6. 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).

  7. 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).

  8. 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
  9. 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
  10. 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).

  11. 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
  12. 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).

  13. A. J. Kaithakkal, Y. Kametani, Y. Hasegawa,
    "Dissimilar heat transfer enhancement in a fully developed laminar channel flow subject to a traveling wave-like wall blowing and suction,"
    Int. J. Heat Mass Transfer 164, 120485 (2021).

  14. サヨツシ キノノヒ。、クタミ カヌノァ。、エ萢ワ キー。、
    。ヨオ。ウ」ウリスャ、ヒ、隍ア゚エノニ篶ョニーヘホョ、ホヘスツャ。ラ
    ニヒワヌウセニウリイサ 63, 52-59 (2021).

  15. 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
  16. K. Fukami, K. Fukagata, and K. Taira,
    "Machine-learning-based spatio-temporal super resolution reconstruction of turbulent flows,"
    J. Fluid Mech. 909, A9 (2021).

  17. 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

  18. 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).

  19. 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).
  20. 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
  21. K. Fukami, K. Fukagata, and K. Taira,
    "Assessment of supervised machine learning methods for fluid flows,"
    Theor. Comput. Fluid Dyn. 34, 497–519 (2020).

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

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

  24. 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)

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

イタ筍ヲニテスクオュサ、ハ、ノ

  1. ソシウ ケッニ。、
    。ヨエチテナェ、ハホョ、セ、ヒツミ、ケ、オ。ウ」ウリスャ、ホアヘム。ラ。、
    ニヒワ・ャ・ケ・ソ。シ・モ・ウリイサ 50(3), 179-184 (2022)。・

  2. トケテォタ ヘホイ。、
    。ヨキラツャ、ネ・キ・゚・螂。シ・キ・逾、ホヘサケ遉ヒ、隍ヌョホョニーセ、ホソ萋遙ラ。、
    オ。ウ」、ホクヲオ 73(12), 911-918 (2021).

  3. ソシウ ケッニ。、
    。ヨヘホョ、ホオ。ウ」ウリスャ、ネタゥク譯ラ。、
    ・ユ・。シ・ノ・ム・。シ・キ・ケ・ニ・ 52(6), 237-241 (2021)。・

  4. ソシウ ケッニ。、ソシクォ ウォ。、
    。ヨオ。ウ」ウリスャスフフ・筵ヌ・、ヘム、、、ソウラソキナェホョ、タゥク讀ヒク、ア、ニ。ラ。、
    ナチヌョ 60(253), 12-15 (2021)。・

  5. ソシウ ケッニ。、
    。ヨオ。ウ」ウリスャ、ホヘホョ、リ、ホアヘム。ラ。、
    ニヒワオ。ウ」ウリイサ 124(1232), 10-13 (2021).

  6. ソシウ ケッニ。、ソシクォ ウォ。、
    。ヨオ。ウ」ウリスャ、ヘム、、、ソヘホョ・モ・テ・ー・ヌ。シ・ソイタマ、ヒク、ア、ニ。ラ。、
    キラツャ、ネタゥク 59(8), 571-576 (2020).

  7. ソケヒワ セュタク。、ソシクォ ウォ。、トケテォタ ーナミ。、ツシナト ケ篷ヒ。、ツシセ ク。、ソシウ ケッニ。、
    。フニテスク。ヘテフワクヲオ in CFD33。ァ。ヨオ。ウ」ウリスャ、ヒエ、ナ、ッ・ヌ。シ・ソウネト・、ヒ、隍PIV 、ホタコナルクセ蝪ラ。、
    、ハ、ャ、 39, 84-87 (2020).

  8. ソシクォ ウォ。、ソシウ ケッニ。、ハソ ヒョノァ。、
    。ヨ・チ・罕ヘ・ヘホョ、ヒ、ェ、ア、オ。ウ」ウリスャ3シ。クオトカイチイタマ。ラ。、
    ニヒワオ。ウ」ウリイホョツホケゥウリノフ逾ヒ・蝪シ・コ・・ソ。シ。ヨホョ、。ラ。、2020ヌッ2キケ, Art. 4 (2020)。・

  9. ソシクォ ウォ。、ソシウ ケッニ。、ハソ ヒョノァ。、
    。フニテスク。ヘテフワクヲオ in ヌッイ2019。ァ。ヨ2シ。クオホョ、セ、リ、ホオ。ウ」ウリスャトカイチ、ホアヘム。ラ。、
    、ハ、ャ、 38, 395-398 (2019).

  10. クタミ カヌノァ。、サヨツシ キノノヒ。、エ萢ワ キー。、
    。フニテスク。ヘオ。ウ」ウリスャ、ホホョツホホマウリクヲオ讀リ、ホアヘム。ァ。ヨハノヘホョタゥク讀ホクホィナェコヌナャイス、ヒク、ア、ソオ。ウ」ウリスャ、ホアヘム。ラ。、
    、ハ、ャ、 38, 329-336 (2019).

  11. トヘクカ ホエヘオ。、タク フノラ。、
    。フニテスク。ヘオ。ウ」ウリスャ、ホホョツホホマウリクヲオ讀リ、ホアヘム。ァ。ヨヘホョハェシチウネサカクサソ萋熙ヒク、ア、ソ CNN イ霖ヌァシア。ラ。、
    、ハ、ャ、 38, 337-343 (2019).

  12. トケテォタ ーナミ。、ソシクォ ウォ。、ツシナト ケ篷ヒ。、ソシウ ケッニ。、
    。フニテスク。ヘテフワクヲオ in CFD32。ァ。ヨオ。ウ」ウリスャ、ヘム、、、ソア゚テシ、ホョ、、ホ・・、・ホ・・コソーヘツクタュ、ホヘスツャ。ラ。、
    、ハ、ャ、 38, 81-84 (2019).

  13. ソシウ ケッニ。、サウヒワ タソ。、エ萢ワ キー。、トケテォタ ヘホイ。、トヘクカ ホエヘオ。、ハ。ナ トセコネ。、シ ヘオフ鬘、タトフレ オチヒ。、
    。フニテスク。ヘテフワクヲオ in ヌッイ2018。ァ。ヨオ。ウ」ウリスャ、ヘム、、、ソヘホョ、ホニテトァテスミシヒ。、ホケステロ、ヒク、ア、ニ。ラ。、
    、ハ、ャ、 37, 524-527 (2018).


Last-modified: 2022-06-22 (ソ) 16:29:54 (6d)
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