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.


2021-12-23Morimoto (M2), , Okochi (M1), and Chida (B4) received Japan Society of Fluid Mechanics Outstanding Young Presenter Award.
2021-12-15ConferenceMorimoto (M2), Nakamura (M2), Okochi (M1), and Chida (B4) made presentations at 35th CFD Symposium (online).
2021-12-13ConferenceHasegawa (M2.5) made a presentation at Machine Learning and the Physical Sciences, Workshop at the 35th Conference on Neural Information Processing Systems (NeurIPS).
2021-12-10Out 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).
2021-12-07PreprintT. Nakamura and K. Fukagata, "Robust training approach of neural networks for fluid flow state estimations," arXiv:2112.02751.
2020-12-01Chida (B4) received the Outstanding Presentation Award at 24th Inter-University Workshop on Turbulence Control (online).
2021-11-26Out now!K. Fukagata, "Machine learning and control of turbulence," J. Jpn. Fluid Power Sys. Soc. 52(6), 237-241 (2021) (in Japanese).
2021-11-16a-deep-learning-techni.jpgVoronoi CNN (Fukami et al., Nat. Mach. Intell. (2021)) has been introduced on TechXplore.
2021-11-12Out 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).
2021-11-12Out 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).
2021-11-04Out now!M. Morimoto, K. Fukami, K. Zhang, and K. Fukagata, "Generalization techniques of neural networks for fluid flow estimation," Neural Comput. Appl. (2021).
2021-10-30Out 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).
2021-10-29Out 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, 945951 (2021)
2021-10-04hasegawa-fdr20.pngOur paper on machine learning of flow around a cylinder (Hasegawa et al., 2020) has been selected as Highlight articles 2020 of Fluid Dynamics Research.
2021-09-30ConferenceNakamura (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).
2021-09-23210923-NaokiMoriya.jpgMoriya (M1) made a presentation at JSFM Annual Meeting 2021 and received the Outstanding Presentation Award.
2021-09-21Out 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).
2021-09-21Out 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).
2021-09-20PreprintM. 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].
2021-09-17Nabae (D3) made a presentation at ETMM13 (Rhodes, Greece (hybrid)).
2020-09-13Nabae (D3) received the Best Presentation Award at 23rd Inter-University Workshop on Turbulence Control (online).
2021-09-06Out 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).
2021-08-30Out 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
2021-08-27ConferenceFukagata, Fukami (Researcher), and Hasegawa (M2.5) made presentation at ICTAM2020+1.
2021-08-02Out 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).
2021-07-29ConferenceFukami (Researcher), Morimoto (M2), and Nakamura (M2) made presentations at 16th US National Congress on Computational Mechanics.
2021-07-19Out now!The lab was introduced in the "Hangaku-Hankyo" corner in the Summer 2021 Issue of PR magazine "Juku."
2021-07-09Out now!K. Fukagata, "Applications of machine learning to turbulence," Nihon Kikai Gakkaishi (J. Jpn Soc. Mech. Eng.) 124(1232), 10-13 (2021) (in Japanese).
2021-07-05whiteKAKENHIlogoS_jp.jpgKAKENHI Project "Creation and implementation of an innovative flow control paradigm utilizing machine learning" has started [heart]
2020-06-18210618-MitsuakiMatsuo.jpgMatsuo (M1) received the Best Presentation Award at 22nd Inter-University Workshop on Turbulence Control (online).
2021-06-18PreprintN. 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].
2021-06-17Out 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).
2021-05-07ConferenceNakamura (M2) and Matsuo (M1) made presentations at The Ninth International Conference on Learning Representations (ILCR 2021).
2021-05-03PreprintT. 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].
2021-04-29Out 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).
2021-04-12Out 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).
2021-04-10ConferenceFukami (Researcher) made a presentation at SoCal Fluids XIV.

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