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K. Fukami, Y. Nabae, K. Kawai, and K. Fukagata, Phys. Rev. Fluids 4, 064603 (2019).
Copyright © 2019 by the American Physical Society.
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深潟 康二,山本 èª ï¼Œå²©æœ¬ è–«ï¼Œé•·è°·å· æ´‹ä»‹ï¼Œå¡šåŽŸ 隆裕,ç¦å³¶ 直哉,守 裕也,é’木 義満,
ã€”ç‰¹é›†ã€•æ³¨ç›®ç ”ç©¶ in 年会2018:「機械å¦ç¿’を用ã„ãŸä¹±æµã®ç‰¹å¾´æŠ½å‡ºæ‰‹æ³•ã®æ§‹ç¯‰ã«å‘ã‘ã¦ã€ï¼Œ
ãªãŒã‚Œ 37, 524-527 (2018).
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).
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).
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日本燃焼å¦ä¼šèªŒ 63, 52-59 (2021).
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).
(Preprint, arXiv:2010.13351 [physics.flu-dyn]).
K. Fukami, K. Fukagata, and K. Taira,
"Machine-learning-based spatio-temporal super resolution reconstruction of turbulent flows,"
J. Fluid Mech. 909, A9 (2021).
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).
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).
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).
K. Fukami, K. Fukagata, and K. Taira,
"Assessment of supervised machine learning methods for fluid flows,"
Theor. Comput. Fluid Dyn. 34, 497–519 (2020).
T. Murata, K. Fukami, and K. Fukagata,
"Nonlinear mode decomposition with convolutional neural networks for fluid dynamics,"
J. Fluid Mech. 882, A13 (2020).
K. Fukami, Y. Nabae, K. Kawai, and K. Fukagata,
"Synthetic turbulent inflow generator using machine learning,"
Phys. Rev. Fluids 4, 064603 (2019).
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)
K. Fukami, K. Fukagata, and K. Taira,
"Super-resolution reconstruction of turbulent flows with machine learning,"
J. Fluid Mech. 870, 106-120 (2019).
深潟 康二,
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日本機械å¦ä¼šèªŒ 124 (7) (2021, 掲載予定).
深潟 康二,深見 開,
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計測ã¨åˆ¶å¾¡ 59(8), 571-576 (2020).
森本 将生,深見 é–‹ï¼Œé•·è°·å· ä¸€ç™»ï¼Œæ‘ç”° 高彬,æ‘上 光,深潟 康二,
ã€”ç‰¹é›†ã€•æ³¨ç›®ç ”ç©¶ in CFD33:「機械å¦ç¿’ã«åŸºã¥ãデータ拡張ã«ã‚ˆã‚‹PIV ã®ç²¾åº¦å‘上ã€ï¼Œ
ãªãŒã‚Œ 39, 84-87 (2020).
深見 開,深潟 康二,平 邦彦,
「ãƒãƒ£ãƒãƒ«ä¹±æµã«ãŠã‘る機械å¦ç¿’3次元超解åƒè§£æžã€ï¼Œ
日本機械å¦ä¼šæµä½“å·¥å¦éƒ¨é–€ãƒ‹ãƒ¥ãƒ¼ã‚ºãƒ¬ã‚¿ãƒ¼ã€Œæµã‚Œã€ï¼Œ2020å¹´2月å·, Art. 4 (2020).
深見 開,深潟 康二,平 邦彦,
ã€”ç‰¹é›†ã€•æ³¨ç›®ç ”ç©¶ in 年会2019:「2次元æµã‚Œå ´ã¸ã®æ©Ÿæ¢°å¦ç¿’超解åƒã®å¿œç”¨ã€ï¼Œ
ãªãŒã‚Œ 38, 395-398 (2019).
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ãªãŒã‚Œ 38, 329-336 (2019).
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〔特集〕機械å¦ç¿’ã®æµä½“力å¦ç ”究ã¸ã®å¿œç”¨ï¼šã€Œä¹±æµç‰©è³ªæ‹¡æ•£æºæŽ¨å®šã«å‘ã‘㟠CNN ç”»åƒèªè˜ã€ï¼Œ
ãªãŒã‚Œ 38, 337-343 (2019).
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ã€”ç‰¹é›†ã€•æ³¨ç›®ç ”ç©¶ in CFD32:「機械å¦ç¿’を用ã„ãŸå††æŸ±å‘¨ã‚Šæµã‚Œã®ãƒ¬ã‚¤ãƒŽãƒ«ã‚ºæ•°ä¾å˜æ€§ã®äºˆæ¸¬ã€ï¼Œ
ãªãŒã‚Œ 38, 81-84 (2019).
深潟 康二,山本 èª ï¼Œå²©æœ¬ è–«ï¼Œé•·è°·å· æ´‹ä»‹ï¼Œå¡šåŽŸ 隆裕,ç¦å³¶ 直哉,守 裕也,é’木 義満,
ã€”ç‰¹é›†ã€•æ³¨ç›®ç ”ç©¶ in 年会2018:「機械å¦ç¿’を用ã„ãŸä¹±æµã®ç‰¹å¾´æŠ½å‡ºæ‰‹æ³•ã®æ§‹ç¯‰ã«å‘ã‘ã¦ã€ï¼Œ
ãªãŒã‚Œ 37, 524-527 (2018).