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.


2022-06-22Out now!Y. Nabae and K. Fukagata, "Drag reduction effect of streamwise traveling wave-like wall deformation with spanwise displacement variation in turbulent channel flow," Flow Turbul. Combust. (2022).
2022-05-31Out now!T. Nakamura and K. Fukagata, "Robust training approach of neural networks for fluid flow state estimations," Int. J. Heat Fluid Flow 96, 108977 (2022).
2022-05-26ConferenceFukagata gave an invited lecture at ParCFD 2022 (Alba, Italy (Hybrid)).
2022-03-31220331.jpgFukagata gave an invited talk at INI Workshop (Cambridge (hybrid)).
2022-03-31AwardChida (B4) received the Best Presentation Award at 61st JSME Kanto Student Union Conference.
2022-03-31Out 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).
2022-03-28220328-1.jpgHasegawa (M2.5) received the JSME Miura Award, and Morimoto (M2) received the Fujiwara Award.
2022-03-23220323-chida-1.jpgChida (B4) received the Certificate of Merit from the Chair of Department of Mechanical Engineering, Keio University.
2022-03-08220308-fujima.jpgFujima (M1) received the Outstanding Presentation Award at 25th Inter-University Workshop on Turbulence Control (online).
2022-03-08Out 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).
2022-02-15Out now!K. Fukagata, News & Views: "Towards quantum computing of turbulence," Nat. Comput. Sci. 2, 68-69 (2022).
2022-02-09PhD defenseNabae (D3)'s PhD defense was held.
2021-12-23AwardMorimoto (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.
2020-12-01AwardChida (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. 34, 3647-3669 (2022).
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.

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Last-modified: 2022-06-22 (Wed) 07:31:23 (6d)
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