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.

News

2022-03-31		Fukagata gave an invited talk at INI Workshop (Cambridge (hybrid)).
2022-03-31	Award	Chida (B4) received the Best Presentation Award at 61st JSME Kanto Student Union Conference.
2022-03-31	Out now!	D. Hiruma, R. Onishi, K. Takahashi, and K. Fukagata, "Sensitivity study on storm modulation through a strategic use of consumer air conditioners," Atmos. Sci. Lett. (2022). http://doi.org/10.1002/asl.1091
2022-03-28		Hasegawa (M2.5) received the JSME Miura Award, and Morimoto (M2) received the Fujiwara Award.
2022-03-23		Chida (B4) received the Certificate of Merit from the Chair of Department of Mechanical Engineering, Keio University.
2022-03-08		Fujima (M1) received the Outstanding Presentation Award at 25th Inter-University Workshop on Turbulence Control (online).
2022-03-08	Out now!	T. Nakamura, K. Fukami, and K. Fukagata, "Identifying key differences between linear stochastic estimation and neural networks for fluid flow regressions," Sci. Rep. 12, 3726 (2022).
2022-02-15	Out now!	K. Fukagata, News & Views: "Towards quantum computing of turbulence," Nat. Comput. Sci. 2, 68-69 (2022).
2022-02-09	PhD defense	Nabae (D3)'s PhD defense was held.
2021-12-23	Award	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	Award	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. 34, 3647-3669 (2022).
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.

Old news