Follow
Kai Fukami
Title
Cited by
Cited by
Year
Super-resolution reconstruction of turbulent flows with machine learning
K Fukami, K Fukagata, K Taira
Journal of Fluid Mechanics 870, 106-120, 2019
5522019
Nonlinear mode decomposition with convolutional neural networks for fluid dynamics
T Murata, K Fukami, K Fukagata
Journal of Fluid Mechanics 882, A13, 2020
2822020
Machine-learning-based spatio-temporal super resolution reconstruction of turbulent flows
K Fukami, K Fukagata, K Taira
Journal of Fluid Mechanics 909, A9, 2021
2152021
Assessment of supervised machine learning methods for fluid flows
K Fukami, K Fukagata, K Taira
Theoretical and Computational Fluid Dynamics 34 (4), 497-519, 2020
1742020
Convolutional neural network based hierarchical autoencoder for nonlinear mode decomposition of fluid field data
K Fukami, T Nakamura, K Fukagata
Physics of Fluids 32 (9), 2020
1602020
Machine-learning-based reduced-order modeling for unsteady flows around bluff bodies of various shapes
K Hasegawa, K Fukami, T Murata, K Fukagata
Theoretical and Computational Fluid Dynamics 34, 367-383, 2020
1582020
Synthetic turbulent inflow generator using machine learning
K Fukami, Y Nabae, K Kawai, K Fukagata
Physical Review Fluids 4 (6), 064603, 2019
1502019
Convolutional neural network and long short-term memory based reduced order surrogate for minimal turbulent channel flow
T Nakamura, K Fukami, K Hasegawa, Y Nabae, K Fukagata
Physics of Fluids 33 (2), 025116, 2021
1382021
CNN-LSTM based reduced order modeling of two-dimensional unsteady flows around a circular cylinder at different Reynolds numbers
K Hasegawa, K Fukami, T Murata, K Fukagata
Fluid Dynamics Research 52 (6), 065501, 2020
1042020
Global field reconstruction from sparse sensors with Voronoi tessellation-assisted deep learning
K Fukami, R Maulik, N Ramachandra, K Fukagata, K Taira
Nature Machine Intelligence 3, 945-951, 2021
1002021
Probabilistic neural networks for fluid flow surrogate modeling and data recovery
R Maulik, K Fukami, N Ramachandra, K Fukagata, K Taira
Physical Review Fluids 5 (10), 104401, 2020
992020
Experimental velocity data estimation for imperfect particle images using machine learning
M Morimoto, K Fukami, K Fukagata
Physics of Fluids 33 (8), 2021
762021
Convolutional neural networks for fluid flow analysis: toward effective metamodeling and low-dimensionalization
M Morimoto, K Fukami, K Zhang, AG Nair, K Fukagata
Theoretical and Computational Fluid Dynamics 35 (5), 633-658, 2021
752021
Sparse identification of nonlinear dynamics with low-dimensionalized flow representations
K Fukami, T Murata, K Zhang, K Fukagata
Journal of Fluid Mechanics 926, A10, 2021
682021
Model order reduction with neural networks: Application to laminar and turbulent flows
K Fukami, K Hasegawa, T Nakamura, M Morimoto, K Fukagata
SN Computer Science 2, 467, 2021
592021
Generalization techniques of neural networks for fluid flow estimation
M Morimoto, K Fukami, K Zhang, K Fukagata
Neural Computing and Applications 34 (5), 3647-3669, 2022
542022
Super-resolution analysis via machine learning: A survey for fluid flows
K Fukami, K Fukagata, K Taira
Theoretical and Computational Fluid Dynamics 37 (4), 421-444, 2023
372023
Reconstructing Three-Dimensional Bluff Body Wake from Sectional Flow Fields with Convolutional Neural Networks
M Matsuo, K Fukami, T Nakamura, M Morimoto, K Fukagata
SN Computer Science 5 (3), 306, 2024
30*2024
Identifying key differences between linear stochastic estimation and neural networks for fluid flow regressions
T Nakamura, K Fukami, K Fukagata
Scientific reports 12 (1), 3726, 2022
23*2022
Super-resolution analysis with machine learning for low-resolution flow data
K Fukami, K Fukagata, K Taira
11th International Symposium on Turbulence and Shear Flow Phenomena (TSFP11), 5, 2019
202019
The system can't perform the operation now. Try again later.
Articles 1–20