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**JSPS KAKENHI (S) (FY2021-2025, No. 21H05007)**

"Creation and implementation of an innovative flow control paradigm utilizing machine learning"**JSPS KAKENHI (A) (FY2018-2020, No. 18H03758)**

"Construction of feature extraction method for turbulence big data by machine learning"

The page for (A) only is here.

K. Fukami, K. Hasegawa, T. Nakamura, M. Morimoto, and K. Fukagata, *SN Comput. Sci.* **2**, 467 (2021).

- 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)

K. Fukagata, K. Iwamoto, and Y. Hasegawa,

"Turbulent drag reduction by streamwise traveling waves of wall-normal forcing,"

*Annu. Rev. Fluid Mech.***56**, 69-90 (2024).K. Fukagata,

"Reduced order modeling of fluid flows using convolutional neural networks,"

*J. Fluid Sci. Technol.***18**, JFST0002 (2023).

T. Ishize, H. Omichi, and K. Fukagata,

"Flow control by a hybrid use of machine learning and control theory,"

arXiv preprint, arXiv:2311.08624 [physics.flu-dyn].R. Miura and K. Fukagata,

"Semi-supervised machine learning model for Lagrangian flow state estimation,"

arXiv preprint, arXiv:2311.08754 [physics.flu-dyn].H. Omichi, T. Ishize, and K. Fukagata,

"Machine learning based dimension reduction for a stable modeling of periodic flow phenomena,"

arXiv preprint, arXiv:2311.08765 [physics.flu-dyn].N. Moriya, K. Fukami, Y. Nabae, M. Morimoto, T. Nakamura, and K. Fukagata,

"Inserting machine-learned virtual wall velocity for large-eddy simulation of turbulent channel flows,"

arXiv preprint, arXiv:2106.09271 [physics.flu-dyn].

Y. Nabae and K. Fukagata,

"Theoretical and numerical analyses of turbulent plane Couette flow controlled using uniform blowing and suction,"

*Int. J. Heat Fluid Flow***106**, 109286 (2024).M. Matsuo, T. Nakamura, M. Morimoto, K. Fukami, and K. Fukagata,

"Reconstructing three-dimensional bluff body wake from sectional flow fields with convolutional neural networks,

*SN Comput. Sci.*(to appear).

(Preprint, arXiv:2103.09020 [physics.flu-dyn])K. Fukami, K. Fukagata, and K. Taira,

"Super-resolution analysis via machine learning: A survey for fluid flows,"

*Theor. Comput. Fluid Dyn.***37**, 421–444 (2023).

(Preprint, arXiv:2301.10937 [physics.flu-dyn])T. Sonoda, Z. Liu, T. Itoh, and Y. Hasegawa,

"Reinforcement learning of control strategies for reducing skin friction drag in a fully developed turbulent channel flow,"

*J. Fluid Mech.***930**, A30 (2023).H. Wang and Y. Hasegawa,

"Multi-objective optimization of actuation waveform for high-precision drop-on-demand inkjet printing,"

*Phys. Fluids***35**, 013318 (2023).M. Atzori, F. Mallor, R. Pozuelo, K. Fukagata, R. Vinuesa, and P. Schlatter,

"A new perspective on skin-friction contributions in adverse-pressure-gradient turbulent boundary layers,"

*Int. J. Heat Fluid Flow***101**, 109117 (2023).T. Ishigami, M. Irikura, and T. Tsukahara,

"Applicability of convolutional neural network for estimation of turbulent diffusion distance from source point,"

*Processes***10**, 860 (2022).T. Ishigami, M. Irikura, T. Tsukahara,

"Machine learning to estimate the mass-diffusion distance from a point source under turbulent conditions,"

*Processes***10**, 860 (2022).M. Morimoto, K. Fukami, R. Maulik, R. Vinuesa, and K. Fukagata,

"Assessments of epistemic uncertainty using Gaussian stochastic weight averaging for fluid-flow regression,"

*Physica D***440**, 133454 (2022).

(Preprint, arXiv:2109.08248 [physics.flu-dyn])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.***109**, 1175–1194 (2022).T. Nakamura and K. Fukagata,

"Robust training approach of neural networks for fluid flow state estimations,"

*Int. J. Heat Fluid Flow***96**, 108977 (2022).

(Preprint, arXiv:2112.02751 [physics.flu-dyn])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).

(Preprint: arXiv:2105.00913 [physics.flu-dyn]).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).- Preprint: arXiv:2101.09985 [physics.flu-dyn]

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).- Preprint: arXiv:2011.11911 [physics.flu-dyn]
- Sample code for Grad-CAM: Available on GitHub

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).K. Fukami, K. Hasegawa, T. Nakamura, M. Morimoto, and K. Fukagata,

"Model order reduction with neural networks: Application to laminar and turbulent flows,"

*SN Comput. Sci.***2**, 467 (2021).M. Morimoto, K. Fukami, and K. Fukagata,

"Experimental velocity data estimation for imperfect particle images using machine learning,"

*Phys. Fluids***33**, 087121 (2021).**Editor's pick**- Sample code: Available on GitHub

K. Fukami, T. Murata, K. Zhang, and K. Fukagata,

"Sparse identification of nonlinear dynamics with low-dimensionalized flow representations,"

*J. Fluid Mech.***926**, A10 (2021).- Sample code: Available on GitHub

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**, 945-951 (2021),- Preprint: arXiv:2101.00554 [physics.flu-dyn]
- Sample code: Available on GitHub

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,"

*Theor. Comput. Fluid Dyn.***35**, 633-658 (2021).- Sample code: Available on GitHub

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).**Editor's pick**- 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).**Highlights of 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.

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.

Last-modified: 2024-01-22 (Mon) 19:50:35