| 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 | 162 | 2020 |
| 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), 2021 | 137 | 2021 |
| 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, 1-16, 2021 | 60 | 2021 |
| 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 | 30 | 2021 |
| 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 | 18 | 2022 |
| Inserting machine-learned virtual wall velocity for large-eddy simulation of turbulent channel flows N Moriya, K Fukami, Y Nabae, M Morimoto, T Nakamura, K Fukagata arXiv preprint arXiv:2106.09271, 2021 | 18 | 2021 |
| Robust training approach of neural networks for fluid flow state estimations T Nakamura, K Fukagata International Journal of Heat and Fluid Flow 96, 108997, 2022 | 14 | 2022 |
| Comparison of linear regressions and neural networks for fluid flow problems assisted with error-curve analysis T Nakamura, K Fukami, K Fukagata arXiv e-prints, arXiv: 2105.00913, 2021 | 7 | 2021 |
| Supervised convolutional networks for vol-umetric data enrichment from limited sec-tional data with adaptive super resolution M Matsuo, K Fukami, T Nakamura, M Morimoto, K Fukagata en. In, 5, 2021 | 3 | 2021 |
| Extension of CNN-LSTM based reduced order surrogate for minimal turbulent channel flow T Nakamura, K Fukami, K Hasegawa, Y Nabae, K Fukagata arXiv e-prints, arXiv: 2010.13351, 2020 | 1 | 2020 |
| Deep learning-based unsteady flow estimation: nonlinear convolution of wakes behind an oscillating cylinder H CHIDA, T NAKAMURA, K ZHANG, K FUKAGATA 数値流体力学シンポジウム講演論文集 (CD-ROM) 35, 5-1, 2021 | | 2021 |
| 機械学習を用いた乱流の状態推定: 入力ノイズに対するロバスト性 中村太一， 深見開， 深潟康二 日本機械学会関東支部総会講演会講演論文集 2021.27, 11C02, 2021 | | 2021 |
| 非線形ダイナミカルシステムに対するニューラルネットワークを用いた異常検知 森本将生， 深見開， 中村太一， 深潟康二 日本機械学会関東支部総会講演会講演論文集 2021.27, 11C07, 2021 | | 2021 |
| Supervised machine learning for wall-modeling in large-eddy simulation of turbulent channel flow N MORIYA, KAI FUKAMI, Y NABAE, M MORIMOTO, T NAKAMURA, ... 数値流体力学シンポジウム講演論文集 (CD-ROM) 34, 10-2, 2020 | | 2020 |
| 階層型 CNN オートエンコーダを用いた流れ場の非線形モードの抽出 中村太一， 深見開， 深潟康二 ながれ: 日本流体力学会誌= Nagare: journal of Japan Society of Fluid …, 2020 | | 2020 |
| Three-dimensional flow field reconstruction from two-dimensional sectional data using machine learning M MATSUO, M MORIMOTO, T NAKAMURA, KAI FUKAMI, K FUKAGATA 数値流体力学シンポジウム講演論文集 (CD-ROM) 34, 6-4, 2020 | | 2020 |
| Extraction of nonlinear modes in fluid flows using a hierarchical convolutional neural network autoencoder T NAKAMURA, KAI FUKAMI, K FUKAGATA ながれ 39 (6), 316-319, 2020 | | 2020 |
| Convolutional neural network based wall modeling for large eddy simulation in a turbulent channel flow N Moriya, K Fukami, Y Nabae, M Morimoto, T Nakamura, K Fukagata APS Division of Fluid Dynamics Meeting Abstracts, R01. 019, 2020 | | 2020 |
| CLUES FOR NOISE ROBUSTNESS OF STATE ESTIMA-TION: ERROR-CURVE QUEST OF NEURAL NETWORK AND LINEAR REGRESSION T Nakamura, K Fukami, K Fukagata | | |
| CNN-AE/LSTM based turbulent flow forecast on low-dimensional latent space T Nakamura, K Fukami, K Hasegawa, Y Nabae, K Fukagata | | |
Koji FukagataKeio University - Department of Mechanical EngineeringDirección de correo verificada de mech.keio.ac.jp
