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Koji Fukagata
Koji Fukagata
Keio University - Department of Mechanical Engineering
Verified email at mech.keio.ac.jp - Homepage
Title
Cited by
Cited by
Year
Contribution of Reynolds stress distribution to the skin friction in wall-bounded flows
K Fukagata, K Iwamoto, N Kasagi
Physics of fluids 14 (11), L73-L76, 2002
8082002
Super-resolution reconstruction of turbulent flows with machine learning
K Fukami, K Fukagata, K Taira
Journal of Fluid Mechanics 870, 106-120, 2019
6972019
Nonlinear mode decomposition with convolutional neural networks for fluid dynamics
T Murata, K Fukami, K Fukagata
Journal of Fluid Mechanics 882, A13, 2020
3292020
A theoretical prediction of friction drag reduction in turbulent flow by superhydrophobic surfaces
K Fukagata, N Kasagi, P Koumoutsakos
Physics of Fluids 18 (5), 051703, 2006
3252006
Machine-learning-based spatio-temporal super resolution reconstruction of turbulent flows
K Fukami, K Fukagata, K Taira
Journal of Fluid Mechanics 909, A9, 2021
2722021
Highly energy-conservative finite difference method for the cylindrical coordinate system
K Fukagata, N Kasagi
Journal of Computational Physics 181 (2), 478-498, 2002
2342002
Microelectromechanical systems–based feedback control of turbulence for skin friction reduction
N Kasagi, Y Suzuki, K Fukagata
Annual Review of Fluid Mechanics 41, 231-251, 2009
2242009
Assessment of supervised machine learning methods for fluid flows
K Fukami, K Fukagata, K Taira
Theoretical and Computational Fluid Dynamics 34, 497-519, 2020
2132020
Convolutional neural network based hierarchical autoencoder for nonlinear mode decomposition of fluid field data
K Fukami, T Nakamura, K Fukagata
Physics of Fluids 32 (9), 2020
1922020
Direct numerical simulation of spatially developing turbulent boundary layers with uniform blowing or suction
Y Kametani, K Fukagata
Journal of Fluid Mechanics 681, 154-172, 2011
1892011
Machine-learning-based reduced-order modeling for unsteady flows around bluff bodies of various shapes
K Hasegawa, K Fukami, T Murata, K Fukagata
Theoretical and Computational Fluid Dynamics 34, 367-383, 2020
1842020
Synthetic turbulent inflow generator using machine learning
K Fukami, Y Nabae, K Kawai, K Fukagata
Physical Review Fluids 4 (6), 064603, 2019
1672019
Convolutional neural network and long short-term memory based reduced order surrogate for minimal turbulent channel flow
T Nakamura, K Fukami, K Hasegawa, Y Nabae, K Fukagata
Physics of Fluids 33 (2), 2021
1632021
Numerical simulation of gas–liquid two-phase flow and convective heat transfer in a micro tube
K Fukagata, N Kasagi, P Ua-arayaporn, T Himeno
International Journal of Heat and Fluid Flow 28 (1), 72-82, 2007
1532007
Effect of uniform blowing/suction in a turbulent boundary layer at moderate Reynolds number
Y Kametani, K Fukagata, R Örlü, P Schlatter
International Journal of Heat and Fluid Flow 55, 132-142, 2015
1482015
Global field reconstruction from sparse sensors with Voronoi tessellation-assisted deep learning
K Fukami, R Maulik, N Ramachandra, K Fukagata, K Taira
Nature Machine Intelligence 3 (11), 945-951, 2021
1432021
Numerical simulation of flow around a circular cylinder having porous surface
H Naito, K Fukagata
Physics of Fluids 24 (11), 2012
1372012
Probabilistic neural networks for fluid flow surrogate modeling and data recovery
R Maulik, K Fukami, N Ramachandra, K Fukagata, K Taira
Physical Review Fluids 5 (10), 104401, 2020
1242020
CNN-LSTM based reduced order modeling of two-dimensional unsteady flows around a circular cylinder at different Reynolds numbers
K Hasegawa, K Fukami, T Murata, K Fukagata
Fluid Dynamics Research 52 (6), 065501, 2020
1202020
Relaminarization of turbulent channel flow using traveling wave-like wall deformation
R Nakanishi, H Mamori, K Fukagata
International Journal of Heat and Fluid Flow 35, 152-159, 2012
1142012
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