| Experimental velocity data estimation for imperfect particle images using machine learning M Morimoto, K Fukami, K Fukagata Physics of Fluids 33, 087121, 2021 | 86 | 2021 |
| 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, 633-658, 2021 | 86 | 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 | 68 | 2021 |
| Generalization techniques of neural networks for fluid flow estimation M Morimoto, K Fukami, K Zhang, K Fukagata Neural Computing and Applications, 2021 | 61 | 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 | 31 | 2021 |
| Assessments of model-form uncertainty using Gaussian stochastic weight averaging for fluid-flow regression M Morimoto, K Fukami, R Maulik, R Vinuesa, K Fukagata arXiv preprint arXiv:2109.08248 [physics.flu-dyn], 2021 | 23* | 2021 |
| 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 | 20 | 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 | 2 | 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 | 1 | 2024 |
| 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 |
| Improvement of PIV by data augmentation based on machine learning M MORIMOTO, KAI FUKAMI, K HASEGAWA, T MURATA, H MURAKAMI, ... ながれ 39 (2), 84-87, 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 |
| 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 |
| Visualization of internal procedure in neural networks for fluid flows M Morimoto, K Fukami, K Fukagata APS Division of Fluid Dynamics Meeting Abstracts, R01. 015, 2020 | | 2020 |
| 機械学習に基づくデータ拡張によるPIVの精度向上 森本将生， 深見開， 長谷川一登， 村田高彬， 村上光， 深潟康二 第33回数値流体力学シンポジウム, B09-1, 2019 | | 2019 |
| EXISTENCE OF C-TYPE VIRAL PARTICLES IN A BIOPSY SPECIMEN OF A LYMPHOMA PATIENT. M SATO, M SAKUDA, M MORIMOTO, K SHIRASUNA, M URADE, ... | | 1977 |
Kai FukamiUniversity of California, Los Angeles在 g.ucla.edu 的电子邮件经过验证
