Kai Fukami
Kai Fukami
University of California, Los Angeles
Dirección de correo verificada de i.softbank.jp - Página principal
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Super-resolution reconstruction of turbulent flows with machine learning
K Fukami, K Fukagata, K Taira
Journal of Fluid Mechanics 870, 106-120, 2019
1302019
Synthetic turbulent inflow generator using machine learning
K Fukami, Y Nabae, K Kawai, K Fukagata
Physical Review Fluids 4 (6), 064603, 2019
622019
Nonlinear mode decomposition with convolutional neural networks for fluid dynamics
T Murata, K Fukami, K Fukagata
Journal of Fluid Mechanics 882, A13, 2020
532020
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
382020
Machine-learning-based spatio-temporal super resolution reconstruction of turbulent flows
K Fukami, K Fukagata, K Taira
Journal of Fluid Mechanics 909, 2021
342021
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 (4), 367-383, 2020
262020
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
26*2020
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), 095110, 2020
202020
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
142020
Experimental velocity data estimation for imperfect particle images using machine learning
M Morimoto, K Fukami, K Fukagata
arXiv preprint arXiv:2005.00756, 2020
122020
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
122019
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
92021
Data-driven reduced order modeling of flows around two-dimensional bluff bodies of various shapes
K Hasegawa, K Fukami, T Murata, K Fukagata
Fluids Engineering Division Summer Meeting 59032, V002T02A075, 2019
82019
Model order reduction with neural networks: Application to laminar and turbulent flows
K Fukami, K Hasegawa, T Nakamura, M Morimoto, K Fukagata
arXiv preprint arXiv:2011.10277, 2020
62020
Convolutional neural networks for fluid flow analysis: toward effective metamodeling and low-dimensionalization
M Morimoto, K Fukami, K Zhang, AG Nair, K Fukagata
arXiv preprint arXiv:2101.02535, 2021
52021
Sparse identification of nonlinear dynamics with low-dimensionalized flow representations
K Fukami, T Murata, K Fukagata
arXiv preprint arXiv:2010.12177, 2020
52020
Global field reconstruction from sparse sensors with voronoi tessellation-assisted deep learning
K Fukami, R Maulik, N Ramachandra, K Fukagata, K Taira
arXiv preprint arXiv:2101.00554, 2021
42021
Generalization techniques of neural networks for fluid flow estimation
M Morimoto, K Fukami, K Zhang, K Fukagata
arXiv preprint arXiv:2011.11911, 2020
42020
Comparison of linear regressions and neural networks for fluid flow problems assisted with error-curve analysis
T Nakamura, K Fukami, K Fukagata
arXiv preprint arXiv:2105.00913, 2021
12021
Supervised convolutional network for three-dimensional fluid data reconstruction from sectional flow fields with adaptive super-resolution assistance
M Matsuo, T Nakamura, M Morimoto, K Fukami, K Fukagata
arXiv preprint arXiv:2103.09020, 2021
12021
El sistema no puede realizar la operación en estos momentos. Inténtalo de nuevo más tarde.
Artículos 1–20