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.


2021-01-12ConferenceFukami (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].
2020-12-23ConferenceArai (M1), Morimoto (M1), Nakamura (M1), Matsuo (B4), and Moriya (B4) made presentations at 34th CFD Symposium (online).
2020-12-21Out now!K. Fukami, K. Fukagata, and K. Taira, "Machine-learning-based spatio-temporal super resolution reconstruction of turbulent flows, J. Fluid Mech. 909, A9 (2021).
2020-12-11ConferenceFukami (Researcher) and Nakamura (M1) made presentations at NeurIPS 2020.
2020-12-04Out now!K. Hasegawa, K. Fukami, T. Murata, and K. Fukagata, "CNN-LSTM based reduced order modeling of two-dimensional unsteady flows around a circular cylinder at different Reynolds numbers," Fluid Dyn. Res. 52, 065501 (2020).
2020-12-02Kanehira (B4) received the Best Presentation Award at 20th Inter-University Workshop on Turbulence Control (online).
2020-11-25PreprintM. Morimoto, K. Fukami, K. Zhang, and K. Fukagata, "Generalization techniques of neural networks for fluid flow estimations," arXiv preprint, arXiv:2011.11911 [physics.flu-dyn].
2020-11-24ConferenceFukami (Researcher), Morimoto (M1), and Moriya (B4) made presentations at APS-DFD 2020 (online).
2020-11-23PreprintK. Fukami, K. Hasegawa, T. Nakamura, M. Morimoto, and K. Fukagata, "Model order reduction with neural networks: Application to laminar and turbulent flows," arXiv preprint, arXiv:2011.10277 [physics.flu-dyn].
2020-10-31ConferenceTwo papers (T. Nakamura et al. "CNN-AE/LSTM based turbulent flow forecast on low-dimensional latent space" / K. Fukami et al. "Probabilistic neural network-based reduced order surrogate for fluid flows") have been accepted for presentation at NeurIPS 2020.
2020-10-27PreprintT. Nakamura, K. Fukami, K. Hasegawa, Y. Nabae, and K. Fukagata, "Extension of CNN-LSTM based reduced order surrogate for minimal turbulent channel flow," arXiv preprint, arXiv:2010.13351 [physics.flu-dyn].
2020-10-26PreprintK. Fukami, T. Murata, and K. Fukagata, "Sparse identification of nonlinear dynamics with low-dimensionalized flow representations," arXiv preprint,arXiv:2010.12177 [physics.flu-dyn].
2020-10-10201010a.jpgFukami (Researcher) et al. received the Certificate of Merit for Outstanding Conference Paper (PRTEC2019) from JSME-TED.
2020-10-10201010b.jpgFukagata received the Certificate of Merit for Thermal Engineering Contribution from JSME-TED.
2020-10-08Out now!R. Maulik, K. Fukami, N. Ramachandra, K. Fukagata, and K. Taira, "Probabilistic neural networks for fluid flow surrogate modeling and data recovery,"Phys. Rev. Fluids 5, 104401 (2020).

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