ML Project
K. Fukami, K. Hasegawa, T. Nakamura, M. Morimoto, and K. Fukagata, SN Comput. Sci. (2021 to appear), arXiv:2011.10277.
Team
Publications
Unpublished Preprints
M. Morimoto, K. Fukami, R. Maulik, R. Vinuesa, and K. Fukagata,
"Assessments of modelform uncertainty using Gaussian stochastic weight averaging for fluidflow regression,"
arXiv preprint, arXiv:2109.08248 [physics.fludyn].
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].
T. Nakamura, K. Fukami, and K. Fukagata,
"Comparison of linear regressions and neural networks for fluid flow problems assisted with errorcurve analysis,"
arXiv preprint, arXiv:2105.00913 [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].
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,"
arXiv preprint, arXiv:2101.09985 [physics.fludyn].
M. Morimoto, K. Fukami, K. Zhang, and K. Fukagata,
"Generalization techniques of neural networks for fluid flow estimation,"
arXiv preprint, arXiv:2011.11911 [physics.fludyn].
 Sample code for GradCAM: Available on GitHub
Journal Articles
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. (to appear).
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
K. Fukami, T. Murata, K. Zhang, and K. Fukagata,
"Sparse identification of nonlinear dynamics with lowdimensionalized flow representations,"
J. Fluid Mech. 926, A10 (2021).
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. (to appear).
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. (2021). https://doi.org/10.1007/s00162021005800
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
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).
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).
