Welcome to Keio Univ. Fukagata Laboratory!

Fukagata laboratory, Department of Mechanical Engineering, Keio University, is a relatively new laboratory, born in 2007.

We perform theoretical, numerical, and experimental studies on flow control and optimization, such as turbulence control (e.g., turbulent friction drag reduction) and suppression of vortex shedding from a body.

Our research is conducted in collaboration with other laboratories.

News

2021-12-23		Morimoto (M2), , Okochi (M1), and Chida (B4) received Japan Society of Fluid Mechanics Outstanding Young Presenter Award.
2021-12-15	Conference	Morimoto (M2), Nakamura (M2), Okochi (M1), and Chida (B4) made presentations at 35th CFD Symposium (online).
2021-12-13	Conference	Hasegawa (M2.5) made a presentation at Machine Learning and the Physical Sciences, Workshop at the 35th Conference on Neural Information Processing Systems (NeurIPS).
2021-12-10	Out now!	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).
2021-12-07	Preprint	T. Nakamura and K. Fukagata, "Robust training approach of neural networks for fluid flow state estimations," arXiv:2112.02751.
2020-12-01		Chida (B4) received the Outstanding Presentation Award at 24th Inter-University Workshop on Turbulence Control (online).
2021-11-26	Out now!	K. Fukagata, "Machine learning and control of turbulence," J. Jpn. Fluid Power Sys. Soc. 52(6), 237-241 (2021) (in Japanese)．
2021-11-16		Voronoi CNN (Fukami et al., Nat. Mach. Intell. (2021)) has been introduced on TechXplore.
2021-11-12	Out now!	S. Miura, M. Ohashi, K. Fukagata, and N. Tokugawa, "Drag reduction by uniform blowing on the pressure surface of an airfoil," AIAA J. (2021). https://doi.org/10.2514/1.J060831
2021-11-12	Out now!	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).
2021-11-04	Out now!	M. Morimoto, K. Fukami, K. Zhang, and K. Fukagata, "Generalization techniques of neural networks for fluid flow estimation," Neural Comput. Appl. (2021). https://doi.org/10.1007/s00521-021-06633-z
2021-10-30	Out now!	K. Fukagata and K. Fukami, "Towards an innovative flow control with machine learning-based reduced-order modeling," J. Heat Transfer Soc. Jpn. 60(253), 12-15 (2021) (in Japanese).
2021-10-29	Out now!	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)
2021-10-04		Our paper on machine learning of flow around a cylinder (Hasegawa et al., 2020) has been selected as Highlight articles 2020 of Fluid Dynamics Research.
2021-09-30	Conference	Nakamura (M2), Morimoto (M2), Kanehira (M1), and Matsuo (M1) made presentations at MMLDT-CSET 2021 (online), and Arai (M2) made a presentation at SAE Powertrains, Fuels & Lubricants Digital Summit (online).
2021-09-23		Moriya (M1) made a presentation at JSFM Annual Meeting 2021 and received the Outstanding Presentation Award.
2021-09-21	Out now!	R. Arai, Y. Nabae, R. Uekusa, H. Murakami, and K. Fukagata, "Numerical modeling of spark path with stretching and short circuit in three-dimensional flow," SAE Paper 2021-01-1164 (2021).
2021-09-21	Out now!	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).
2021-09-20	Preprint	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].
2021-09-17		Nabae (D3) made a presentation at ETMM13 (Rhodes, Greece (hybrid)).
2020-09-13		Nabae (D3) received the Best Presentation Award at 23rd Inter-University Workshop on Turbulence Control (online).
2021-09-06	Out now!	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).
2021-08-30	Out now!	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
2021-08-27	Conference	Fukagata, Fukami (Researcher), and Hasegawa (M2.5) made presentation at ICTAM2020+1.
2021-08-02	Out now!	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. (2021). https://doi.org/10.1007/s00162-021-00580-0
2021-07-29	Conference	Fukami (Researcher), Morimoto (M2), and Nakamura (M2) made presentations at 16th US National Congress on Computational Mechanics.
2021-07-19	Out now!	The lab was introduced in the "Hangaku-Hankyo" corner in the Summer 2021 Issue of PR magazine "Juku."
2021-07-09	Out now!	K. Fukagata, "Applications of machine learning to turbulence," Nihon Kikai Gakkaishi (J. Jpn Soc. Mech. Eng.) 124(1232), 10-13 (2021) (in Japanese).
2021-07-05		KAKENHI Project "Creation and implementation of an innovative flow control paradigm utilizing machine learning" has started
2020-06-18		Matsuo (M1) received the Best Presentation Award at 22nd Inter-University Workshop on Turbulence Control (online).
2021-06-18	Preprint	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].
2021-06-17	Out now!	M. Badri Ghomizad, H. Kor, and K. Fukagata, "A structured adaptive mesh refinement strategy with a sharp interface direct-forcing immersed boundary method for moving boundary problems," J. Fluid Sci. Technol. 16, JFST0014 (2021).
2021-05-07	Conference	Nakamura (M2) and Matsuo (M1) made presentations at The Ninth International Conference on Learning Representations (ILCR 2021).
2021-05-03	Preprint	T. Nakamura, K. Fukami, and K. Fukagata, "Comparison of linear regressions and neural networks for fluid flow problems assisted with error-curve analysis," arXiv preprint, arXiv:2105.00913 [physics.flu-dyn].
2021-04-29	Out now!	M. Ohashi, K. Fukagata, and N. Tokugawa, "Adjoint-based sensitivity analysis for airfoil flow control aiming at lift-to-drag ratio improvement," AIAA J. (2021). https://doi.org/10.2514/1.J060415
2021-04-12	Out now!	M. Badri Ghomizad, H. Kor, and K. Fukagata, "A sharp interface direct-forcing immersed boundary method using the moving least square approximation," J. Fluid Sci. Technol. 16, JFST0013 (2021).
2021-04-10	Conference	Fukami (Researcher) made a presentation at SoCal Fluids XIV.

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