譴ー蟄ヲ縺ョ繝壹シ繧ク


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


遐皮ゥカ菴灘宛

遐皮ゥカ逶ョ逧

荳サ縺ェ遐皮ゥカ謌先棡

縺セ縺ィ繧∬ィ倅コ

  1. 豺ア貎 蠎キ莠鯉シ
    縲檎糞縺ソ霎シ縺ソ繝九Η繝シ繝ゥ繝ォ繝阪ャ繝医Ρ繝シ繧ッ繧堤畑縺縺滓オ√l蝣エ縺ョ菴取ャ。蜈蛹悶サ謗ィ螳壹サ蛻カ蠕。縲搾シ
    譌・譛ャ讖滓「ー蟄ヲ莨夊ィ育ョ怜鴨蟄ヲ驛ィ髢繝九Η繝シ繧ケ繝ャ繧ソ繝シシ君o. 71, 20-23 (2024).

繝ャ繝薙Η繝シ隲匁枚

  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).

譛ェ蜃コ迚医ョ繝励Ξ繝励Μ繝ウ繝

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

  2. 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].

  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].

遐皮ゥカ隲匁枚

  1. T. Ishize, H. Omichi, and K. Fukagata,
    "Flow control by a hybrid use of machine learning and control theory,"
    Int. J. Numer. Meth. Heat Fluid Flow 34, 3253-3277 (2024).
    (Preprint, arXiv:2311.08624 [physics.flu-dyn])

  2. 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).

  3. M. Matsuo, K. Fukami, T. Nakamura, M. Morimoto, 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])

  4. K. Hirose, K. Fukudome, H. Mamori, and M. Yamamoto,
    "Three-dimensional trajectory and impingement simulation of ice crystals considering state changes on the rotor blade of an axial fan,"
    Aerospace 11, 2 (2024).

  5. D. Chen, P. Kumar, Y. Kametani, and Y. Hasegawa,
    "Multi-objective topology optimization of heat transfer surface using level-set method and adaptive mesh refinement in OpenFOAM,"
    Int. J. Heat Mass Transfer 221, 125099 (2024).

  6. T. Tsukahara, T. Ishigami, and M. Irikura,
    "Deep learning estimation of scalar source distance for different turbulent and molecular diffusion environments,"
    J. Fluid Sci. Technol. 19, JFST0020 (2024).

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

  8. K. Matsubara, A. Mitsuishi, K. Iwamoto, and A. Murata,
    "Prediction of pulsating turbulent pipe flow by deep learning with generalization capability,"
    Int. J. Heat Fluid Flow 104, 109214 (2023).

  9. Y. Yugeta, K. Uji, Y. Itoh, and Y. Hasegawa,
    "Prediction of optimal control input in a fully developed turbulent channel flow by machine learning,"
    J. Fluid Sci. Technol. 18, JFST0033 (2023).

  10. H. Mamori, Y. Nabae, S. Fukuda, and H. Gotoda,
    "Dynamic state of low-Reynolds-number turbulent channel flow,"
    Phys. Rev. E 108, 025105 (2023).

  11. 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).

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

  13. 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).

  14. 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).

  15. 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).

  16. 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])

  17. 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).

  18. 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])

  19. 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]).

  20. 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).

  21. 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).

  22. 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).

  23. 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).

  24. 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
  25. 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
  26. 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).

  27. 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
  28. 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).

  29. 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).

  30. 蠢玲搗 謨ャ蠖ャシ悟臥浹 證∝スヲシ悟イゥ譛ャ 阮ォシ
    縲梧ゥ滓「ー蟄ヲ鄙偵↓繧医k蜀邂。蜀閼亥虚荵ア豬√ョ莠域クャ縲
    譌・譛ャ辯辟シ蟄ヲ莨夊ェ 63, 52-59 (2021).

  31. 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
  32. K. Fukami, K. Fukagata, and K. Taira,
    "Machine-learning-based spatio-temporal super resolution reconstruction of turbulent flows,"
    J. Fluid Mech. 909, A9 (2021).

  33. 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

  34. 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).

  35. 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).
  36. 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
  37. K. Fukami, K. Fukagata, and K. Taira,
    "Assessment of supervised machine learning methods for fluid flows,"
    Theor. Comput. Fluid Dyn. 34, 497–519 (2020).

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

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

  40. 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)

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

隗」隱ャ繝サ迚ケ髮險倅コ九↑縺ゥ

  1. 豺ア貎 蠎キ莠鯉シ
    縲梧ゥ滓「ー蟄ヲ鄙偵ョ蝓コ遉弱→豬∽ス灘撫鬘後∈縺ョ蠢懃畑縲搾シ
    繧ソ繝シ繝懈ゥ滓「ー 51(11), 10-16 (2023)シ

  2. 蝪壼次 髫陬,
    縲檎イ伜シセ諤ァ豬∽ス薙d荵ア豬∵僑謨」繧貞ッセ雎。縺ィ縺励◆豺ア螻、蟄ヲ鄙偵搾シ
    譌・譛ャ讖滓「ー蟄ヲ莨壽オ∽ス灘キ・蟄ヲ驛ィ髢繝九Η繝シ繧ケ繝ャ繧ソ繝シ縲梧オ√l縲, 2022蟷エ11譛亥捷, Art. 5 (2022).

  3. 豺ア貎 蠎キ莠鯉シ
    縲檎糞縺ソ霎シ縺ソ繝九Η繝シ繝ゥ繝ォ繝阪ャ繝医Ρ繝シ繧ッ繧堤畑縺縺滓オ∽ス灘エ縺ョ菴取ャ。蜈蛹悶→谺謳肴ュ蝣ア謗ィ螳壹搾シ
    譌・譛ャ鬚ィ蟾・蟄ヲ莨夊ェ 47(3), 215-220 (2022)シ

  4. 豺ア貎 蠎キ莠鯉シ
    縲悟渕遉守噪縺ェ豬√l蝣エ縺ォ蟇セ縺吶k讖滓「ー蟄ヲ鄙偵ョ蠢懃畑縲搾シ
    譌・譛ャ繧ャ繧ケ繧ソ繝シ繝薙Φ蟄ヲ莨夊ェ 50(3), 179-184 (2022)シ

  5. 髟キ隹キ蟾 豢倶サ具シ
    縲瑚ィ域クャ縺ィ繧キ繝溘Η繝ャ繝シ繧キ繝ァ繝ウ縺ョ陞榊粋縺ォ繧医k辭ア豬∝虚蝣エ縺ョ謗ィ螳壹搾シ
    讖滓「ー縺ョ遐皮ゥカ 73(12), 911-918 (2021).

  6. 豺ア貎 蠎キ莠鯉シ
    縲御ケア豬√ョ讖滓「ー蟄ヲ鄙偵→蛻カ蠕。縲搾シ
    繝輔Ν繝シ繝峨ヱ繝ッ繝シ繧キ繧ケ繝繝 52(6), 237-241 (2021)シ

  7. 豺ア貎 蠎キ莠鯉シ梧キア隕 髢具シ
    縲梧ゥ滓「ー蟄ヲ鄙堤クョ邏繝「繝繝ォ繧堤畑縺縺滄擠譁ー逧豬√l蛻カ蠕。縺ォ蜷代¢縺ヲ縲搾シ
    莨晉ア 60(253), 12-15 (2021)

  8. 豺ア貎 蠎キ莠鯉シ
    縲梧ゥ滓「ー蟄ヲ鄙偵ョ荵ア豬√∈縺ョ蠢懃畑縲搾シ
    譌・譛ャ讖滓「ー蟄ヲ莨夊ェ 124(1232), 10-13 (2021).

  9. 豺ア貎 蠎キ莠鯉シ梧キア隕 髢具シ
    縲梧ゥ滓「ー蟄ヲ鄙偵r逕ィ縺縺滉ケア豬√ン繝繧ー繝繝シ繧ソ隗」譫舌↓蜷代¢縺ヲ縲搾シ
    險域クャ縺ィ蛻カ蠕。 59(8), 571-576 (2020).

  10. 譽ョ譛ャ 蟆逕滂シ梧キア隕 髢具シ碁聞隹キ蟾 荳逋サシ梧搗逕ー 鬮伜スャシ梧搗荳 蜈会シ梧キア貎 蠎キ莠鯉シ
    縲皮音髮縲墓ウィ逶ョ遐皮ゥカ in CFD33シ壹梧ゥ滓「ー蟄ヲ鄙偵↓蝓コ縺・縺上ョ繝シ繧ソ諡。蠑オ縺ォ繧医kPIV 縺ョ邊セ蠎ヲ蜷台ク翫搾シ
    縺ェ縺後l 39, 84-87 (2020).

  11. 豺ア隕 髢具シ梧キア貎 蠎キ莠鯉シ悟ケウ 驍ヲ蠖ヲシ
    縲後メ繝」繝阪Ν荵ア豬√↓縺翫¢繧区ゥ滓「ー蟄ヲ鄙3谺。蜈雜隗」蜒剰ァ」譫舌搾シ
    譌・譛ャ讖滓「ー蟄ヲ莨壽オ∽ス灘キ・蟄ヲ驛ィ髢繝九Η繝シ繧ケ繝ャ繧ソ繝シ縲梧オ√l縲搾シ2020蟷エ2譛亥捷, Art. 4 (2020)

  12. 豺ア隕 髢具シ梧キア貎 蠎キ莠鯉シ悟ケウ 驍ヲ蠖ヲシ
    縲皮音髮縲墓ウィ逶ョ遐皮ゥカ in 蟷エ莨2019シ壹2谺。蜈豬√l蝣エ縺ク縺ョ讖滓「ー蟄ヲ鄙定カ隗」蜒上ョ蠢懃畑縲搾シ
    縺ェ縺後l 38, 395-398 (2019).

  13. 蜈臥浹 證∝スヲシ悟ソ玲搗 謨ャ蠖ャシ悟イゥ譛ャ 阮ォシ
    縲皮音髮縲墓ゥ滓「ー蟄ヲ鄙偵ョ豬∽ス灘鴨蟄ヲ遐皮ゥカ縺ク縺ョ蠢懃畑シ壹悟」∽ケア豬∝宛蠕。縺ョ蜉ケ邇逧譛驕ゥ蛹悶↓蜷代¢縺滓ゥ滓「ー蟄ヲ鄙偵ョ蠢懃畑縲搾シ
    縺ェ縺後l 38, 329-336 (2019).

  14. 蝪壼次 髫陬包シ悟キ晏哨 髱門、ォシ
    縲皮音髮縲墓ゥ滓「ー蟄ヲ鄙偵ョ豬∽ス灘鴨蟄ヲ遐皮ゥカ縺ク縺ョ蠢懃畑シ壹御ケア豬∫黄雉ェ諡。謨」貅先耳螳壹↓蜷代¢縺 CNN 逕サ蜒剰ェ崎ュ倥搾シ
    縺ェ縺後l 38, 337-343 (2019).

  15. 髟キ隹キ蟾 荳逋サシ梧キア隕 髢具シ梧搗逕ー 鬮伜スャシ梧キア貎 蠎キ莠鯉シ
    縲皮音髮縲墓ウィ逶ョ遐皮ゥカ in CFD32シ壹梧ゥ滓「ー蟄ヲ鄙偵r逕ィ縺縺溷譟ア蜻ィ繧頑オ√l縺ョ繝ャ繧、繝弱Ν繧コ謨ー萓晏ュ俶ァ縺ョ莠域クャ縲搾シ
    縺ェ縺後l 38, 81-84 (2019).

  16. 豺ア貎 蠎キ莠鯉シ悟アア譛ャ 隱シ悟イゥ譛ャ 阮ォシ碁聞隹キ蟾 豢倶サ具シ悟。壼次 髫陬包シ檎ヲ丞ウカ 逶エ蜩会シ悟ョ 陬穂ケ滂シ碁搨譛ィ 鄒ゥ貅シ
    縲皮音髮縲墓ウィ逶ョ遐皮ゥカ in 蟷エ莨2018シ壹梧ゥ滓「ー蟄ヲ鄙偵r逕ィ縺縺滉ケア豬√ョ迚ケ蠕エ謚ス蜃コ謇区ウ輔ョ讒狗ッ峨↓蜷代¢縺ヲ縲搾シ
    縺ェ縺後l 37, 524-527 (2018).


Last-modified: 2024-09-03 (轣ォ) 12:50:22