Collaborators
Collaborators
Kunihiko Taira (Professor, UCLA) [Google Scholar] [ResearchGate] [ORCID]
Supevisor for my Ph.D. degree.
Koji Fukagata (Professor, Keio University) [Google Scholar] [ORCID]
Supevisor for my bachelor's and master's degrees.
Ken Kawai (Former Graduate Student, Keio University)
K. Fukami, Y. Nabae, K. Kawai, K. Fukagata, “Synthetic turbulent inflow generator using machine learning,” Physical Review Fluids, 4 (064603), 2019
Yusuke Nabae (Assistant Professor, Tokyo University of Science) [Google Scholar]
K. Fukami, Y. Nabae, K. Kawai, K. Fukagata, “Synthetic turbulent inflow generator using machine learning,” Physical Review Fluids, 4 (064603), 2019
Takaaki Murata (Former Graduate Student, Keio University)
T. Murata, K. Fukami, K. Fukagata, “Nonlinear mode decomposition with convolutional neural networks for fluid dynamics,” Journal of Fluid Mechanics, 882, A13, 2020
Kazuto Hasegawa (Former Graduate Student, Keio University/Politecnico di Milano) [ResearchGate]
K. Hasegawa, K. Fukami, T. Murata, K. Fukagata, “Machine-learning-based reduced-order modeling for unsteady fluid flows with various bluff bodies,” Theoretical and Computational Fluid Dynamics, 34 (4), 367--383, 2020
Masaki Morimoto (Former Graduate Student, Keio University) [Google Scholar] [ResearchGate] [ORCID]
M. Morimoto, K. Fukami, K. Fukagata, “Experimental velocity data estimation for imperfect particle images using machine learning,” Physics of Fluids, 33, 087121, 2021
Hikaru Murakami (Former Graduate Student, Keio University)
M. Morimoto, K. Fukami, K. Hasegawa, T. Murata, H. Murakami, K. Fukagata, [SI] Focus on CFD33: “Improvement of PIV by data augmentation based on machine learning,” Nagare-Journal of Japan Society of Fluid Mechanics, 39 (2), 84-87 (invited), 2020
Romit Maulik (Assistant Professor, Pennsylvania State University) [Google Scholar] [ResearchGate] [ORCID]
R. Maulik, K. Fukami, N. Ramachandra, K. Fukagata, K. Taira, “Probabilistic neural networks for fluid flow surrogate modeling and data recovery,” Physical Review Fluids, 5 (104401), 2020
Nesar Ramachandra (Computational Scientist, Argonne National Laboratory) [Google Scholar] [ORCID]
R. Maulik, K. Fukami, N. Ramachandra, K. Fukagata, K. Taira, “Probabilistic neural networks for fluid flow surrogate modeling and data recovery,” Physical Review Fluids, 5 (104401), 2020
Taichi Nakamura (Former Graduate Student, Keio University) [Google Scholar] [ResearchGate] [ORCID]
T. Nakamura, K. Fukami, K. Hasegawa, Y. Nabae, K. Fukagata, "Convolutional neural network and long short-term memory based reduced order surrogate for minimal turbulent channel flow," Physics of Fluids, 33, 025116, 2021
Aditya Nair (Assistant Professor, University of Nevada, Reno) [Google Scholar] [ResearchGate]
M. Morimoto, K. Fukami, K. Zhang, A. G. Nair, K. Fukagata, ``Convolutional neural networks for fluid flow analysis: toward effective metamodeling and low-dimensionalization," Theoretical and Computational Fluid Dynamics, 35 (5), 633-658, 2021
Kai Zhang (Associate Professor, Shanghai Jiao Tong University) [Google Scholar] [ResearchGate]
K. Fukami, T. Murata, K. Zhang, K. Fukagata, "Sparse identification of nonlinear dynamics with low-dimensionalized flow representations," Journal of Fluid Mechanics, 926, A10, 2021 (preprint, arXiv:2010.12177 [physics.flu-dyn]), [code]
Mitsuaki Matsuo (Former Graduate Student, Keio University) [Google Scholar] [ResearchGate]
M. Matsuo, K. Fukami, T. Nakamura, M. Morimoto, K. Fukagata, "Reconstructing three-dimensional bluff body wake from sectional flow fields with convolutional neural networks," SN Computer Science, 5, 306, 2024 (preprint, arXiv:2103.09020 [physics.flu-dyn])
Naoki Moriya (Former Graduate Student, Keio University/KTH Royal Institute of Technology) [Google Scholar]
N. Moriya, K. Fukami, Y. Nabae, M. Morimoto, T. Nakamura, K. Fukagata, "Inserting machine-learned virtual wall velocity for large-eddy simulation of turbulent channel flow," preprint, arXiv:2106.09271 [physics.flu-dyn], 2021
Ricardo Vinuesa (Associate Professor, KTH Royal Institute of Technology) [Google Scholar] [ResearchGate]
M. Morimoto, K. Fukami, R. Maulik, R. Vinuesa, K. Fukagata, "Assessments of epistemic uncertainty using Gaussian stochastic weight averaging for fluid-flow regression," Physica D: Nonlinear Phenomena, 440, 133454, 2022 (preprint, arXiv:2109.08248 [physics.flu-dyn])
Yonghong Zhong (Graduate Student, UCLA) [Google Scholar] [ResearchGate]
Y. Zhong, K. Fukami, B. An, K. Taira, ``Sparse sensor reconstruction of vortex-impinged airfoil wake with machine learning," Theoretical and Computational Fluid Dynamics, 37, 269--287, 2023 (preprint, arXiv:2305.05147 [physics.flu-dyn])
Byungjin An (Ebara Corporation) [Google Scholar] [ResearchGate]
K. Fukami, B. An, M. Nohmi, M. Obuchi, K. Taira, "Machine-learning-based reconstruction of turbulent vortices from sparse pressure sensors in a pump sump," Journal of Fluids Engineering, 144(12), 121501, 2022
Vedasri Godavarthi (Postdoctoral Research Associate, Johns Hopkins University) [Google Scholar] [ResearchGate]
K. Fukami, V. Godavarthi, Y. Zhong, C.-A. Yeh, K. Taira, ``Time-varying broadcast mode analysis for airfoil wake dynamics," in IUTAM Symposium on Data-driven modeling and optimization in fluid mechanics, Aarhus, Denmark, Jun 2022
Chi-An Yeh (Assistant Professor, North Carolina State University) [Google Scholar] [ResearchGate]
K. Fukami, V. Godavarthi, Y, Zhong, C.-A. Yeh, K. Taira, ``Time-varying broadcast mode analysis for airfoil wake dynamics," in IUTAM Symposium on Data-driven modeling and optimization in fluid mechanics, Aarhus, Denmark, Jun 2022
Motohiko Nohmi (Ebara Corporation) [ResearchGate]
K. Fukami, B. An, M. Nohmi, M. Obuchi, K. Taira, "Machine-learning-based reconstruction of turbulent vortices from sparse pressure sensors in a pump sump," Journal of Fluids Engineering, 144(12), 121501, 2022
Masashi Obuchi (Ebara Corporation) [ResearchGate]
K. Fukami, B. An, M. Nohmi, M. Obuchi, K. Taira, "Machine-learning-based reconstruction of turbulent vortices from sparse pressure sensors in a pump sump," Journal of Fluids Engineering, 144(12), 121501, 2022
Vishal Anantharaman (Graduate student, Caltech)
V. Anantharaman, J. Feldkamp, K. Fukami, K. Taira, ``Image and video compression of fluid flow data," Theoretical and Computational Fluid Dynamics, 37, 61--82, 2023
Jason Feldkamp (Former Undergraduate Student, UCLA)
V. Anantharaman, J. Feldkamp, K. Fukami, K. Taira, ``Image and video compression of fluid flow data," Theoretical and Computational Fluid Dynamics, 37, 61--82, 2023
Hiroto Odaka (Graduate Student, UCLA) [Google Scholar]
H. Odaka, K. Fukami, K. Taira, ``Data-driven latent variable representations for plunging airfoil wakes," in Review, 2023
Luke R. Smith (Research Associate, Texas Advanced Computing Center at the University of Texas at Austin) [Google Scholar]
L. R. Smith, K. Fukami, G. Sedky, A. R. Jones, K. Taira, ``A cyclic perspective on transient gust encounters through the lens of persistent homology," Journal of Fluid Mechanics, 980, A18, 2024
Girguis Sedky (Postdoctoral Research Associate, Princeton University) [Google Scholar] [ResearchGate]
L. R. Smith, K. Fukami, G. Sedky, A. R. Jones, K. Taira, ``A cyclic perspective on transient gust encounters through the lens of persistent homology," Journal of Fluid Mechanics, 980, A18, 2024
Anya R. Jones (Professor, UCLA) [Google Scholar] [ResearchGate]
L. R. Smith, K. Fukami, G. Sedky, A. R. Jones, K. Taira, ``A cyclic perspective on transient gust encounters through the lens of persistent homology," Journal of Fluid Mechanics, 980, A18, 2024
Dashuai Chen (Postdoctoral Research Associate, Westlake University)
D. Chen, F. Kaiser, J.-C. Hu, D. E. Rival, K. Fukami, K. Taira, ``Sparse pressure-based machine learning approach for aerodynamic loads estimation during gust encounters," AIAA Journal, 62, 1, 275-290, 2024
Frieder Kaiser (RheEnergise Ltd.) [Google Scholar] [ResearchGate]
D. Chen, F. Kaiser, J.-C. Hu, D. E. Rival, K. Fukami, K. Taira, ``Sparse pressure-based machine learning approach for aerodynamic loads estimation during gust encounters," AIAA Journal, 62, 1, 275-290, 2024
Jia Cheng (Winston) Hu (RheEnergise Ltd.) [Google Scholar]
D. Chen, F. Kaiser, J.-C. Hu, D. E. Rival, K. Fukami, K. Taira, ``Sparse pressure-based machine learning approach for aerodynamic loads estimation during gust encounters," AIAA Journal, 62, 1, 275-290, 2024
David E. Rival (Professor, TU Braunschweig) [Google Scholar] [ResearchGate]
D. Chen, F. Kaiser, J.-C. Hu, D. E. Rival, K. Fukami, K. Taira, ``Sparse pressure-based machine learning approach for aerodynamic loads estimation during gust encounters," AIAA Journal, 62, 1, 275-290, 2024
Susumu Goto (Professor, Osaka University) [Google Scholar] [ORCID]
K. Fukami, S. Goto, K. Taira, ``Data-driven nonlinear turbulent flow scaling with Buckingham Pi variables," Journal of Fluid Mechanics, 984, R4, 2024
Hiroya Nakao (Professor, Science Tokyo) [Google Scholar] [ResearchGate] [ORCID]
K. Fukami, H. Nakao, K. Taira, ``Data-driven transient lift attenuation for extreme vortex gust-airfoil interactions," Journal of Fluid Mechanics, 992, A17, 2024
Koichiro Yawata (Ph.D. student, Science Tokyo) [Google Scholar]
K. Yawata, K. Fukami, K. Taira, H. Nakao, ``Phase autoencoder for limit-cycle oscillators," Chaos, 34, 063111, 2024 (preprint, arXiv:2403.06992 [nlin.AO]) (Selected as an Editor's Pick.) [code]
Jonathan Tran (Graduate Student, UCLA) [Google Scholar]
J. Tran, K. Fukami, K. Inada, D. Umehara, Y. Ono, K. Ogawa, K. Taira,``Aerodynamics-guided machine learning for design optimization of electric vehicles," Communications Engineering, 3, 174, 2024
Kenta Inada (Honda Motor Co., Ltd.)
J. Tran, K. Fukami, K. Inada, D. Umehara, Y. Ono, K. Ogawa, K. Taira,``Aerodynamics-guided machine learning for design optimization of electric vehicles," Communications Engineering, 3, 174, 2024
Daisuke Umehara (Honda Motor Co., Ltd.)
J. Tran, K. Fukami, K. Inada, D. Umehara, Y. Ono, K. Ogawa, K. Taira,``Aerodynamics-guided machine learning for design optimization of electric vehicles," Communications Engineering, 3, 174, 2024
Yoshimichi Ono (Honda Motor Co., Ltd.)
J. Tran, K. Fukami, K. Inada, D. Umehara, Y. Ono, K. Ogawa, K. Taira,``Aerodynamics-guided machine learning for design optimization of electric vehicles," Communications Engineering, 3, 174, 2024
Kenta Ogawa (Honda Motor Co., Ltd.)
J. Tran, K. Fukami, K. Inada, D. Umehara, Y. Ono, K. Ogawa, K. Taira,``Aerodynamics-guided machine learning for design optimization of electric vehicles," Communications Engineering, 3, 174, 2024
Alec J. Linot (Postdoctoral Research Associate, UCLA) [Google Scholar]
K. Taira, K. Fukami, L. R. Smith, Y. Zhong, A. J. Linot, H. Odaka, B. Lopez-Doriga, ``Data-driven analysis, modeling, and control of extreme aerodynamic flows," in the EuroMech Colloquium on Data-Driven Fluid Dynamics and the 2nd ERCOFTAC Workshop on Machine Learning for Fluid Dynamics, London, UK, Apr 2025.
Barbara Lopez-Doriga (Postdoctoral Research Associate, UCLA) [Google Scholar]
K. Taira, K. Fukami, L. R. Smith, Y. Zhong, A. J. Linot, H. Odaka, B. Lopez-Doriga, ``Data-driven analysis, modeling, and control of extreme aerodynamic flows," in the EuroMech Colloquium on Data-Driven Fluid Dynamics and the 2nd ERCOFTAC Workshop on Machine Learning for Fluid Dynamics, London, UK, Apr 2025.
Yuta Iwatani (Ph. D. student, Tohoku University) [Google Scholar]
K. Fukami, Y. Iwatani, S. Maejima, H. Asada, S. Kawai, “Compact representation of transonic airfoil buffet flows with observable-augmented machine learning,” in Review, 2025
Soju Maejima (Ph. D. student, Tohoku University) [Google Scholar]
K. Fukami, Y. Iwatani, S. Maejima, H. Asada, S. Kawai, “Compact representation of transonic airfoil buffet flows with observable-augmented machine learning,” in Review, 2025
Hiroyuki Asada (Assistant Professor, Tohoku University) [Google Scholar]
K. Fukami, Y. Iwatani, S. Maejima, H. Asada, S. Kawai, “Compact representation of transonic airfoil buffet flows with observable-augmented machine learning,” in Review, 2025
Soshi Kawai (Professor, Tohoku University) [Google Scholar] [ORCID]
K. Fukami, Y. Iwatani, S. Maejima, H. Asada, S. Kawai, “Compact representation of transonic airfoil buffet flows with observable-augmented machine learning,” in Review, 2025
Lennart Rohlfs (Research Assistant, TU Berlin) [Google Scholar]
L. Rohlfs, J. Pelz, N. A. K. Doan, K. Fukami, B. Steinfurth, “Fourier-embedded physics-informed neural networks for assimilation of particle tracking velocimetry data," for the 2nd Lagrangian Particle Tracking and Data Assimilation Challenges at a workshop in the ISFV21 and ISPIV2025 conferences, Tokyo, Japan, Jun 2025.
Jonas Pelz (Graduate student, TU Berlin)
L. Rohlfs, J. Pelz, N. A. K. Doan, K. Fukami, B. Steinfurth, “Fourier-embedded physics-informed neural networks for assimilation of particle tracking velocimetry data," for the 2nd Lagrangian Particle Tracking and Data Assimilation Challenges at a workshop in the ISFV21 and ISPIV2025 conferences, Tokyo, Japan, Jun 2025.
Nguyen Anh Knoa Doan (Assistant Professor, TU Delft) [Google Scholar]
L. Rohlfs, J. Pelz, N. A. K. Doan, K. Fukami, B. Steinfurth, “Fourier-embedded physics-informed neural networks for assimilation of particle tracking velocimetry data," for the 2nd Lagrangian Particle Tracking and Data Assimilation Challenges at a workshop in the ISFV21 and ISPIV2025 conferences, Tokyo, Japan, Jun 2025.
Ben Steinfurth (Postdoctoral Research Associate, TU Berlin) [Google Scholar]
L. Rohlfs, J. Pelz, N. A. K. Doan, K. Fukami, B. Steinfurth, “Fourier-embedded physics-informed neural networks for assimilation of particle tracking velocimetry data," for the 2nd Lagrangian Particle Tracking and Data Assimilation Challenges at a workshop in the ISFV21 and ISPIV2025 conferences, Tokyo, Japan, Jun 2025.