JSPS KAKENHI Grant-in-Aid for Scientific Research (A) (FY2018-2020, No. 18H03758) ## Construction of feature extraction method for turbulence big data by machine learningK. Fukami, Y. Nabae, K. Kawai, and K. Fukagata, ## Team- Principal Investigator: Koji Fukagata (Keio Univ)
- Co-Investigators: Makoto Yamamoto (Tokyo Univ Sci), Yosuke Hasegawa (Univ Tokyo), Kaoru Iwamoto (Tokyo Univ Agri Technol), Takahiro Tsukahara (Tokyo Univ Sci), Naoya Fukushima (Tokai Univ)，Hiroya Mamori (Univ Electro-Commun)
- Co-Investigator ("Renkei Kenkyusha"): Yoshimitsu Aoki (Keio Univ)
- Research Collaborator: Kunihiko Taira (UCLA), Staffs and students in PI and Co-Investigators' groups
## Publications## Unpublished PreprintsY. 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].M. 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].K. 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].M. Morimoto, K. Fukami, K. Zhang, and K. Fukagata, "Generalization techniques of neural networks for fluid flow estimation," arXiv preprint, arXiv:2011.11911 [physics.flu-dyn].- Sample code for Grad-CAM: Available on GitHub
K. 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].K. Fukami, T. Murata, and K. Fukagata, "Sparse identification of nonlinear dynamics with low-dimensionalized flow representations," arXiv preprint, arXiv:2010.12177 [physics.flu-dyn].M. Morimoto, K. Fukami, and K. Fukagata, "Experimental velocity data estimation for imperfect particle images using machine learning," arXiv preprint, arXiv:2005.00756 [physics.flu-dyn].
## Journal ArticlesT. 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). (Preprint, arXiv:2010.13351 [physics.flu-dyn]).- Sample code: Available on GitHub
K. Fukami, K. Fukagata, and K. Taira, "Machine-learning-based spatio-temporal super resolution reconstruction of turbulent flows,"
*J. Fluid Mech.***909**, A9 (2021).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).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).- K. Fukami, T. Nakamura, and K. Fukagata,
"Convolutional neural network based hierarchical autoencoder for nonlinear mode decomposition of fluid field data,"
*Phys. Fluids***32**, 095110 (2020).
- Sample code: HierarchicalAE_fig3.py
K. Hasegawa, K. Fukami, T. Murata, and K. Fukagata, "Machine-learning-based reduced order modeling for unsteady flows around bluff bodies of various shapes,"
*Theor. Comput. Fluid Dyn.***34**, 367-383 (2020).- Sample code: Available on GitHub
K. Fukami, K. Fukagata, and K. Taira, "Assessment of supervised machine learning methods for fluid flows,"
*Theor. Comput. Fluid Dyn.***34**, 497519 (2020).T. Murata, K. Fukami, and K. Fukagata, "Nonlinear mode decomposition with convolutional neural networks for fluid dynamics,"
*J. Fluid Mech.***882**, A13 (2020).- Sample code: MD-CNN-AE.py
- Sample flow field data is automatically downloaded. The details are noted in this code.
K. Fukami, Y. Nabae, K. Kawai, and K. Fukagata, "Synthetic turbulent inflow generator using machine learning,"
*Phys. Rev. Fluids***4**, 064603 (2019).- Sample code: Available on GitHub
- Animation: Machine-Learned Turbulence Generator, Ver. 2 (MLTG2).
Q. Wang, Y. Hasegawa, and T. Zaki, "Spatial reconstruction of steady scalar sources from remote measurements in turbulent flow,"
*J. Fluid Mech.***870**, 316-352 (2019)K. Fukami, K. Fukagata, and K. Taira, "Super-resolution reconstruction of turbulent flows with machine learning,"
*J. Fluid Mech.***870**, 106-120 (2019).- Sample code: Available at UCLA Taira Lab.
## Conference Proceedings- R. Yamaguchi, A. Mitsuishi, T. Shimura, K. Iwamoto, and A. Murata,
"Prediction of time evolution of vortex structure in pulsating turbulent pipe flow by deep learning," The 29th International Symposium on Transport Phenomena, Honolulu, Hawaii, USA (2018).
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Last-modified: 2021-02-26 (Fri) 02:24:41 (3d)

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