| Super-resolution reconstruction of turbulent flows with machine learning K Fukami, K Fukagata, K Taira Journal of Fluid Mechanics 870, 106-120, 2019 | 691 | 2019 |
| Nonlinear mode decomposition with convolutional neural networks for fluid dynamics T Murata, K Fukami, K Fukagata Journal of Fluid Mechanics 882, A13, 2020 | 326 | 2020 |
| Machine-learning-based spatio-temporal super resolution reconstruction of turbulent flows K Fukami, K Fukagata, K Taira Journal of Fluid Mechanics 909, A9, 2021 | 270 | 2021 |
| 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 | 213 | 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), 2020 | 190 | 2020 |
| 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 | 184 | 2020 |
| Synthetic turbulent inflow generator using machine learning K Fukami, Y Nabae, K Kawai, K Fukagata Physical Review Fluids 4 (6), 064603, 2019 | 167 | 2019 |
| 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 | 164 | 2021 |
| 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 | 142 | 2021 |
| 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 | 121 | 2020 |
| 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 | 120 | 2020 |
| 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 | 103 | 2021 |
| Experimental velocity data estimation for imperfect particle images using machine learning M Morimoto, K Fukami, K Fukagata Physics of Fluids 33 (8), 2021 | 91 | 2021 |
| 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 | 90 | 2021 |
| 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 | 83 | 2023 |
| 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 | 72 | 2022 |
| 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 | 69 | 2021 |
| 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 | 33* | 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 | 32* | 2022 |
| Assessments of epistemic uncertainty using Gaussian stochastic weight averaging for fluid-flow regression M Morimoto, K Fukami, R Maulik, R Vinuesa, K Fukagata Physica D: Nonlinear Phenomena 440, 133454, 2022 | 29 | 2022 |
Koji FukagataKeio University - Department of Mechanical EngineeringVerified email at mech.keio.ac.jp
