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JSPS KAKENHI Grant-in-Aid for Scientific Research (A) (FY2018-2020, No. 18H03758)

K. Fukami, Y. Nabae, K. Kawai, and K. Fukagata, *Phys. Rev. Fluids* **4**, 064603 (2019).

Copyright © 2019 by the American Physical Society.

- 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

W. Kobayashi, T. Shimura, A. Mitsuishi, K. Iwamoto, and A. Murata,

"Prediction of the drag reduction effect of pulsating pipe flow based on machine learning,"

*Int. J. Heat Fluid Flow***88**, 108783 (2021).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).

(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**, 497–519 (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.
- Sample code with sample data are also available here.

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.

- 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).

Last-modified: 2024-03-01 (Fri) 13:09:02