ML Project
K. Fukami, K. Hasegawa, T. Nakamura, M. Morimoto, and K. Fukagata, SN Comput. Sci. 2, 467 (2021).
Team
Publications
Unpublished Preprints
N. Moriya, K. Fukami, Y. Nabae, M. Morimoto, T. Nakamura, and K. Fukagata,
"Inserting machinelearned virtual wall velocity for largeeddy simulation of turbulent channel flows,"
arXiv preprint, arXiv:2106.09271 [physics.fludyn].
M. Matsuo, T. Nakamura, M. Morimoto, K. Fukami, and K. Fukagata,
"Supervised convolutional network for threedimensional fluid data reconstruction from sectional flow fields with adaptive superresolution assistance,"
arXiv preprint, arXiv:2103.09020 [physics.fludyn].
 Sample code: Available on GitHub
K. Fukami, K. Fukagata, and K. Taira,
"Superresolution analysis via machine learning: A survey for fluid flows,"
arXiv preprint, arXiv:2301.10937 [physics.fludyn].
Journal Articles
T. Ishigami, M. Irikura, T. Tsukahara,
"Machine learning to estimate the massdiffusion 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 fluidflow regression,"
Physica D 440, 133454 (2022).
(Preprint, arXiv:2109.08248 [physics.fludyn])
Y. Nabae and K. Fukagata,
"Drag reduction effect of streamwise traveling wavelike wall deformation with spanwise displacement variation in turbulent channel flow,"
Flow Turbul. Combust. 109, 11751194 (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.fludyn])
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.fludyn]).
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).
M. Morimoto, K. Fukami, K. Zhang, and K. Fukagata,
"Generalization techniques of neural networks for fluid flow estimation,"
Neural Comput. Appl. 34, 36473669 (2022).
Y. Nabae and K. Fukagata,
"Bayesian optimization of traveling wavelike 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 lowdimensionalized 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 tessellationassisted deep learning,"
Nat. Mach. Intell. 3, 945951 (2021),
M. Morimoto, K. Fukami, K. Zhang, A. G. Nair, and K. Fukagata,
"Convolutional neural networks for fluid flow analysis: toward effective metamodeling and lowdimensionalization,"
Theor. Comput. Fluid Dyn. 35, 633658 (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 shortterm 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,
"Machinelearningbased spatiotemporal super resolution reconstruction of turbulent flows,"
J. Fluid Mech. 909, A9 (2021).
K. Hasegawa, K. Fukami, T. Murata, and K. Fukagata,
"CNNLSTM based reduced order modeling of twodimensional 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).
K. Hasegawa, K. Fukami, T. Murata, and K. Fukagata,
"Machinelearningbased reduced order modeling for unsteady flows around bluff bodies of various shapes,"
Theor. Comput. Fluid Dyn. 34, 367383 (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: MDCNNAE.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).
Q. Wang, Y. Hasegawa, and T. Zaki,
"Spatial reconstruction of steady scalar sources from remote measurements in turbulent flow,"
J. Fluid Mech. 870, 316352 (2019)
K. Fukami, K. Fukagata, and K. Taira,
"Superresolution reconstruction of turbulent flows with machine learning,"
J. Fluid Mech. 870, 106120 (2019).
