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Welcome to Keio Univ. Fukagata Laboratory!†

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

Our research interests are numerical simulation and mathematical modeling of complex heat and fluid flow phenomena including turbulent flows and development of advanced control methods for such flow phenomena. The research area is being expanded toward establishment of design methodology for thermo-fluids systems by integrating control theories, optimization methods, machine learning, and large-scale flow simulation techniques.

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News†

2023-11-21		Chida (M2), Ishize (M2), Miura (M2), and Omichi (M1) made presentations at APS-DFD 2023 (Washington DC).
2023-11-16	Preprint	T. Ishize, H. Omichi, and K. Fukagata, "Flow control by a hybrid use of machine learning and control theory," arXiv preprint, arXiv:2311.08624 [physics.flu-dyn].
2023-11-16	Preprint	R. Miura and K. Fukagata, "Semi-supervised machine learning model for Lagrangian flow state estimation," arXiv preprint, arXiv:2311.08754 [physics.flu-dyn].
2023-11-16	Preprint	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].
2023-10-30	Out now!	K. Fukagata, "Fundamentals of machine learning and its applicatios to fluid flow problems," Turbomachinery 51(11), 10-16 (2023) (in Japanese).
2023-09-30		Goto (M1) received the Outstanding Presentation Award at 31st Inter-University Workshop on Turbulence Control (Tokyo).
2023-09-29	Out now!	JSME-FED "The hot topic on study, technological development and R&D in fluids engineering fields" (Y. Nabae & K. Fukagata "Bayesian optimization of traveling wave-like wall deformation for friction drag reduction in turbulent channel flow")
2023-09-22		Goto (M1) made a presentation at 2023 JSFM Annual Meeting (Tokyo).
2023-09-09		Iwasawa (M1) and Goto (M1) made presentations at Mechanical Engineering Congress, Japan 2023 (Tokyo).
2023-07-11		Omichi (M1) and Suzuki (M1) made presentations at AJKFED 2023 (Osaka). Also, Fukagata received the Certificate of Merit for Fluids　Engineering Contribution from JSME-FED.
2023-07-07	Conference	Fukagata gave an invited lecture at Applied Mathematics Symposium: Artificial Intelligence Meets Fluid Dynamics, India (Online).
2023-06-23		Faculty of Science and Technology Softball Tournament was held after a long time, and we participated as a team "Fu." (lost….)
2023-06-17	Out now!	K. Fukami, K. Fukagata, and K. Taira, "Super-resolution analysis via machine learning: A survey for fluid flows," Theor. Comput. Fluid Dyn. (2023). https://doi.org/10.1007/s00162-023-00663-0
2023-06-09	Out now!	K. Fukagata, "Fluid Mechanics in the 21st Century," Gakumon no Susume, Faculty of Science and Technology, Keio University, 2023-6 (2023) (in Japanese).
2023-05-18		Miura (M2) made a presentation at Keio-Kasetsart Joint Workshop in Mechanical Engineering.
2023-05-10	Out now!	H. Omichi, H. Chida, T. Ishize, M. Matsuo, and K. Fukagata, Selected Researches in CFD36: "Improvement of particle image velocimetry using machine learning without DNS data," Nagare - J. Jpn. Soc. Fluid Mech. 42, 83-86 (2023) (in Japanese).
2023-05-10	Out now!	K. Fukagata, Preface: "Report on the 36th Computational Fluid Dynamics Symposium (CFD36)," Nagare - J. Jpn. Soc. Fluid Mech. 42, 50-51 (2023) (in Japanese).
2023-04-26	Out now!	K. Fukagata, Review: "Reduced order modeling of fluid flows using convolutional neural networks," J. Fluid Sci. Technol. 18, JFST0002 (2023).
2023-04-20		Nabae (Alumni) & Fukagata's paper received JSME Award (Paper).