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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.

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

2021-05-07ConferenceNakamura (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). https://doi.org/10.2514/1.J060415
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.
2020-03-26210326.jpg3 Master students have graduated, and Fukami (graduated in Sep. 2020) received the Fujiwara Award.
2020-03-23210323.jpg6 Bachelor students have graduated; Matsuo (B4) received the Fujiwara Award, and Okochi (B4) received the Hatakeyama Award.
2020-03-19Nakamura (B1) received the Best Presentation Award at 21st Inter-University Workshop on Turbulence Control (online).
2021-03-17PreprintM. Matsuo, T. Nakamura, M. Morimoto, K. Fukami, and K. Fukagata, "Supervised convolutional network for three-dimensional fluid data reconstruction from sectional flow fields with adaptive super-resolution assistance," arXiv preprint, arXiv:2103.09020 [physics.flu-dyn].
2021-03-17Invited talkNakamura (M1) gave an invited talk at DataLearning Working Group Seminar, Imperial College London (Online).
2021-03-11ConferenceMorimoto (M1) and Nakamura (M1) made presentations at 27th JSME Kanto Branch Conference (Online).
2021-03-10ConferenceFujima (B4), Kanehira (B4), Matsuo (B4), Moriya (B4), Okochi (B4), and Sato (B4) made presentations at 60th JSME Kanto Student Union Conference (Online).
2021-03-09Invited talkFukami (Researcher) gave an invited talk at DataLearning Working Group Seminar, Imperial College London (Online).
2021-03-05ConferenceFukami (Researcher) and Morimoto (M1) made presentations at 2021 SIAM Conference on Computational Science and Engineering (Online).
2021-02-25Out now!T. Nakamura, K. Fukami, K. Hasegawa, Y. Nabae, and K. Fukagata, "Convolutional neural network and long short-term memory based reduced order surrogate for minimal turbulent channel flow,"Phys. Fluids 33, 025116 (2021). Editor's picks
2021-01-25PreprintY. Morita, S. Rezaeiravesh, N. Tabatabaei, R. Vinuesa, K. Fukagata, and P. Schlatter, "Applying Bayesian optimization with Gaussian process regression to computational fluid dynamics problems," arXiv preprint, arXiv:2101.09985 [physics.flu-dyn].
2021-01-12ConferenceFukami (Researcher), Ohashi (M2), and Morimoto (M1) made presentations at 14th WCCM-ECCOMAS Congress (online).
2021-01-11PreprintM. Morimoto, K. Fukami, K. Zhang, A. G. Nair, and K. Fukagata, "Convolutional neural networks for fluid flow analysis: toward effective metamodeling and low-dimensionalization," arXiv preprint, arXiv:2101.02535 [physics.flu-dyn].
2021-01-07Out now![SI] Selected Research in JSFM Annual Meeting 2020
Y. Nabae and K. Fukagata, "Parameter dependence of turbulent friction drag reduction effect by wave-machine-like traveling waves," Nagare 39, 312-315 (2020) (in Japanese).
T. Nakamura, K. Fukami, and K. Fukagata, "Extraction of nonlinear modes in fluid flows using a hierarchical convolutional neural network autoencoder," Nagare 39, 316-319 (2020) (in Japanese).
D. Hiruma, R. Onishi, K. Fukagata, and K. Takahashi, "Numerical analysis to quantify the storm controllability," Nagare 39, 324-327 (2020) (in Japanese).
2021-01-05PreprintK. Fukami, R. Maulik, N. Ramachandra, K. Fukagata, and K. Taira, "Global field reconstruction from sparse sensors with Voronoi tessellation-assisted deep learning," arXiv preprint, arXiv:2101.00554 [physics.flu-dyn].

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Last-modified: 2021-05-11 (Tue) 02:33:47 (36d)
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