Publications
Publications
: Journal papers (numbering) / Conference papers (non-numbering)
K. Fukami, L. Smith, K. Taira, “Extreme vortex-gust airfoil interactions at Reynolds number 5,000," in Review, 2025
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
K. Fukami, K. Taira, “Observable-augmented manifold learning for multi-source turbulent flow data,” Journal of Fluid Mechanics, accepted, 2025
H. Odaka, K. Fukami, K. Taira, “Plunging airfoil wakes in low-order latent space coordinates,” in AIAA Aviation Forum 2025, Las Vegas, Nevada, USA, July 2025.
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.
K. Fukami, K. Taira, “Single-snapshot machine learning for super-resolution of turbulence,” Journal of Fluid Mechanics, 1001, A32, 2024. (preprint, arXiv:2409.04923 [physics.flu-dyn])
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 (Selected as a Featured article.)
K. Fukami, H. Nakao, K. Taira, “Data-driven transient lift attenuation for extreme vortex gust-airfoil interactions,” Journal of Fluid Mechanics, 992, A17, 2024 (preprint, arXiv:2403.00263 [physics.flu-dyn])
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]
K. Fukami, S. Goto, K. Taira, “Data-driven nonlinear turbulent flow scaling with Buckingham Pi variables,” Journal of Fluid Mechanics, 984, R4, 2024 (preprint, arXiv:2402.17990 [physics.flu-dyn]) (Selected as an Editor's Pick of JFM Rapids. [link1],[link2])
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]), [code]
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 (preprint, arXiv:2306.15829 [physics.flu-dyn])
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
K. Fukami, K. Taira, “Data-driven modeling, sensing, and control of extreme vortex-airfoil interactions,” in AIAA Aviation Forum 2024, Las Vegas, Nevada, USA, July-Aug 2024, AIAA 2024-4531.
K. Fukami, K. Taira, “Extreme aerodynamics of vortex impingement: Machine-learning-based compression and situational awareness,” 13th International Symposium on Turbulence and Shear Flow Phenomena (TSFP13), Montréal, Canada, June 2024, Paper 114. [PDF]
K. Fukami, K. Taira, “Grasping extreme aerodynamics on a low-dimensional manifold,” Nature Communications, 14, 6480, 2023 (preprint, arXiv:2305.08024 [physics.flu-dyn]) (Selected as an Editors' Highlight.) [code], [UCLA-MAE news], [AIAA 2023 Year-in-Review]
K. Fukami, K. Fukagata, K. Taira, “Super-resolution analysis via machine learning: A survey for fluid flows,” Theoretical and Computational Fluid Dynamics, 37, 421--444 (invited), 2023 (preprint, arXiv:2301.10937 [physics.flu-dyn])
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])
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 (preprint, arXiv:2301.00078 [physics.flu-dyn])
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
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, 440, 133454, 2022 (preprint, arXiv:2109.08248 [physics.flu-dyn])
M. Morimoto, K. Fukami, R. Maulik, R. Vinuesa, K. Fukagata, [SI] Focus on CFD35: “Model-form uncertainty quantification in neural-network-based fluid-flow estimation,” Nagare-Journal of Japan Society of Fluid Mechanics, 41 (2), 89-92 (invited), 2022
T. Nakamura, K. Fukami, K. Fukagata, “Identifying key differences between linear stochastic estimation and neural networks for fluid flow regressions,” Scientific Reports, 12, 3726, 2022 (preprint, arXiv:2105.00913 [physics.flu-dyn])
M. Morimoto, K. Fukami, K. Zhang, K. Fukagata, “Generalization techniques of neural networks for fluid flow estimation,” Neural Computing and Applications, 34, 3647-3669, 2022 (preprint, arXiv:2011.11911 [physics.flu-dyn]), [code]
K. Fukami, K. Taira, “Learning the nonlinear manifold of extreme aerodynamics,” in Machine Learning and the Physical Sciences, Workshop at the 36th Conference on Neural Information Processing Systems (NeurIPS), Dec 2022. [Paper] [Poster]
Y. Zhong, K. Fukami, B. An, K. Taira, “Machine-learning-based reconstruction of transient vortex-airfoil wake interaction,” AIAA Aviation Forum 2022, Chicago, Illinois, Jun 2022, AIAA 2022-3234.
K. Fukami, B. An, M. Nohmi, M. Obuchi, K. Taira, “Machine-learning-based turbulent state estimation from pressure sensors in a pump sump,” in the 86th conference of turbomachinery society of Japan, 8608, May 2022.
K. Fukagata, K. Fukami, [SI] Data-Driven Science of Thermal/Fluid Engineering: “Towards an innovative flow control with machine learning-based reduced-order modeling,” Journal of the Heat Transfer Society of Japan, 60 (253), 12-15 (invited), 2021
K. Fukami, R. Maulik, N. Ramachandra, K. Fukagata, K. Taira, “Global field reconstruction from sparse sensors with Voronoi tessellation-assisted deep learning,” Nature Machine Intelligence, 3, 945-951, 2021 (preprint, arXiv:2101.00554 [physics.flu-dyn]), [code], [UCLA-MAE news], [TechXplore]
K. Fukami, K. Hasegawa, T. Nakamura, M. Morimoto, K. Fukagata, “Model order reduction with neural networks: Application to laminar and turbulent flows,” SN Computer Science, 2, 467, 2021 (preprint, arXiv:2011.10277 [physics.flu-dyn])
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]
M. Morimoto, K. Fukami, K. Fukagata, “Experimental velocity data estimation for imperfect particle images using machine learning,” Physics of Fluids, 33, 087121, 2021 (preprint, arXiv:2005.00756 [physics.flu-dyn]), [code] (Selected as an Editor's Pick.)
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 (preprint, arXiv:2101.02535 [physics.flu-dyn]), [code]
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 (preprint, arXiv:2010.13351 [physics.flu-dyn]), [code] (Selected as an Editor's Pick.)
K. Fukami, K. Fukagata, K. Taira, “Machine-learning-based spatio-temporal super resolution reconstruction of turbulent flows,” Journal of Fluid Mechanics, 909, A9, 2021 (preprint, arXiv:2004.11566 [physics.flu-dyn])
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
K. Hasegawa, K. Fukami, K. Fukagata, “Visualization of nonlinear modal structures for three-dimensional unsteady fluid flows with customized decoder design,” in Machine Learning and the Physical Sciences, Workshop at the 35th Conference on Neural Information Processing Systems (NeurIPS), Dec 2021. [PDF] [Poster]
T. Nakamura, K. Fukami, K. Fukagata, “Convolutional neural network-based global field recovery from sparse sensors of transitional boundary layer flow,” in 35th CFD symposium, online presentation, Dec 2021.
M. Morimoto, K. Fukami, R. Maulik, R. Vinuesa, K. Fukagata, “Model form uncertainty quantification of neural network-based fluid flow estimation,” in 35th CFD symposium, online presentation, Dec 2021.
N. Moriya, M. Morimoto, K. Fukami, K. Hasegawa, K. Fukagata, “Hierarchical neural network for spatial reconstruction of fluid flow fields,” in The Japan Society of Fluid Mechanics Annual Meeting 2021, online presentation, Sep 2021.
K. Fukami, T. Murata, K. Zhang, S. Kanehira, K. Fukagata, “Time-series analysis of fluid flow dynamics using latent variables with sparse regressions: towards data-driven flow controls,” in JSME Mechanical Engineering Congress, 2021 Japan (MECJ-21), online, Sep 2021, J063-10.
T. Nakamura, K. Fukami, K. Fukagata, “Fluid flow state estimation from sparse sensor measurements using convolutional neural network,” in JSME Mechanical Engineering Congress, 2021 Japan (MECJ-21), online, Sep 2021, J063-11.
K. Hasegawa, K. Fukami, K. Fukagata, “Applications of convolutional neural network-based nonlinear mode decomposition to three-dimensional fluid flows,” in JSME Mechanical Engineering Congress, 2021 Japan (MECJ-21), online, Sep 2021, J063-12.
M. Matsuo, K. Fukami, T. Nakamura, M. Morimoto, K. Fukagata, “Supervised convolutional networks for volumetric data enrichment from limited sectional data with adaptive super resolution,” in International Conference on Learning Representation (ICLR) workshop, Deep Learning for Simulation (SIMDL), May 2021. [PDF] [Poster]
T. Nakamura, K. Fukami, K. Fukagata, “Clues for noise robustness of state estimation: Error-curve quest of neural network and linear regression,” in International Conference on Learning Representation (ICLR) workshop, Deep Learning for Simulation (SIMDL), May 2021. [PDF] [Poster]
M. Morimoto, K. Fukami, T. Nakamura, K. Fukagata, “Neural network-based anomaly detections for nonlinear dynamical systems,” in 27th JSME Kanto Union conference, online, Mar 2021, Paper 11C07.
T. Nakamura, K. Fukami, K. Fukagata, “Machine learning based state estimation of turbulent flows: robustness for noisy input,” in 27th JSME Kanto Union conference, online, Mar 2021, Paper 11C02.
N. Moriya, K. Fukami, Y. Nabae, M. Morimoto, T. Nakamura, K. Fukagata, “Supervised machine learning based data-driven wall modeling for large-eddy simulation in a turbulent channel flow,” in 60th JSME Kanto Student Union Conference, Tokyo, Japan, Mar 2021, Paper 702.
M. Matsuo, M. Morimoto, T. Nakamura, K. Fukami, K. Fukagata, “Convolutional neural network based three-dimensional data reconstruction from two-dimensional sectional data with adaptive sampling,” in 60th JSME Kanto Student Union Conference, Tokyo, Japan, Mar 2021, Paper 701.
S. Kanehira, K. Fukami, K. Hasegawa, T. Nakamura, M. Morimoto, K. Fukagata, “Feedback control of unsteady fluid flows using an autoencoder with sparse identification of nonlinear dynamics,” in 60th JSME Kanto Student Union Conference, Tokyo, Japan, Mar 2021, Paper 705.
T. Nakamura, K. Fukami, K. Fukagata, [SI] Focus on JSFM Annual Meeting 2020: “Extraction of nonlinear modes in fluid flows with a hierarchical convolutional neural network autoencoder,” Nagare-Journal of Japan Society of Fluid Mechanics, 39 (6), 316-319, (invited), 2020
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 (preprint, arXiv:2005.04271 [physics.flu-dyn]), [code]
K. Hasegawa, K. Fukami, T. Murata, K. Fukagata, “CNN-LSTM based reduced order modeling of two-dimensional unsteady flows around a circular cylinder at different Reynolds numbers,” Fluid Dynamics Research, 52, 065501, 2020 (Selected as a Highlight Article in 2020.)
K. Fukami, T. Nakamura, K. Fukagata, “Convolutional neural network based hierarchical autoencoder for nonlinear mode decomposition of fluid field data,” Physics of Fluids, 32, 095110, 2020 (preprint, arXiv:2006.06977 [physics.comp-ph]), [code]
K. Fukagata, K. Fukami, “Toward turbulence big data analysis using machine learning,” Journal of the Society of Instrument and Control Engineers, 59 (8), 571-576 (invited), 2020
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
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 (invited), 2020 (preprint, arXiv:2003.07548 [physics.flu-dyn]), [code]
K. Fukami, K. Fukagata, K. Taira, “Assessment of supervised machine learning methods for fluid flows,” Theoretical and Computational Fluid Dynamics, 34 (4), 497--519 (invited), 2020 (preprint, arXiv:2001.09618 [physics.flu-dyn])
K. Fukami, K. Fukagata, K. Taira, [SI] Focus on the conference of fluid engineering division: “Machine-learned three-dimensional super-resolution analysis of turbulent channel flow,” Nagare-News letter of Japan Society of Mechanical Engineers: Fluids Engineering Division, Art. 4, (invited), 2020 [English] [Japanese]
T. Murata, K. Fukami, K. Fukagata, “Nonlinear mode decomposition with convolutional neural networks for fluid dynamics,” Journal of Fluid Mechanics, 882, A13, 2020 (preprint: arXiv:1906.04029 [physics.comp-ph]), [code]
T. Nakamura, K. Fukami, K. Fukagata, “Machine learning-aided state estimation in a turbulent channel flow and its robustness for sensor information,” in 34th CFD symposium, Okinawa, Japan, Dec 2020, Paper F07-2.
N. Moriya, K. Fukami, Y. Nabae, M. Morimoto, T. Nakamura, K. Fukagata, “Supervised machine learning for wall-modeling in large eddy simulation of turbulent channel flows,” in 34th CFD symposium, Okinawa, Japan, Dec 2020, Paper F10-2.
M. Matsuo, M. Morimoto, T. Nakamura, K. Fukami, K. Fukagata, “Three-dimensional flow field reconstruction from two-dimensional sectional data using machine learning,” in 34th CFD symposium, Okinawa, Japan, Dec 2020, Paper 06-4.
M. Morimoto, K. Fukami, K. Zhang, K. Fukagata, “Toward practical machine learning and fluid flow regressions: perspective on interpretability and generalizability,” in 34th CFD symposium, Okinawa, Japan, Dec 2020, Paper 06-1.
T. Nakamura, K. Fukami, K. Hasegawa, Y. Nabae, K. Fukagata, “CNN-AE/LSTM based turbulent flow forecast on low-dimensional latent space,” in Machine Learning and the Physical Sciences, Workshop at the 34th Conference on Neural Information Processing Systems (NeurIPS), Dec 2020 [PDF] [YouTube] [Poster]
K. Fukami, R. Maulik, N. Ramachandra, K. Fukagata, K. Taira, “Probabilistic neural network-based reduced-order surrogate for fluid flows,” in Machine Learning and the Physical Sciences, Workshop at the 34th Conference on Neural Information Processing Systems (NeurIPS), Dec 2020 [arXiv] [PDF] [YouTube] [Poster]
T. Nakamura, K. Fukami, K. Fukagata, “Extraction of nonlinear modes in fluid flows with a hierarchical convolutional neural network autoencoder,” in The Japan Society of Fluid Mechanics Annual Meeting 2020, online presentation, Sep 2020, Paper 076.
M. Morimoto, K. Fukami, K. Hasegawa, T. Nakamura, K. Fukagata, “Investigation of autoencoder-based low dimensionalization for various flow fields,” in The Japan Society of Fluid Mechanics Annual Meeting 2020, online presentation, Sep 2020, Paper 074.
T. Nakamura, K. Fukami, K. Hasegawa, T. Murata, Y. Nabae, K. Fukagata, “Application of machine learning for reduced order modeling of turbulent channel flow,” in 59th JSME Kanto Student Union Conference, Tokyo, Japan, Mar 2020, Paper 1301.
M. Morimoto, K. Fukami, K. Hasegawa, T. Murata, H. Murakami, K. Fukagata, “Proposal of machine learning based particle image velocimetry,” in 59th JSME Kanto Student Union Conference, Tokyo, Japan, Mar 2020, Paper 1407.
K. Fukami, K. Fukagata, K. Taira, [SI] Focus on JSFM Annual Meeting 2019: “Applications of machine-learned super-resolution algorithm for two-dimensional flow fields,” Nagare-Journal of Japan Society of Fluid Mechanics, 38 (6), 395-398 (invited), 2019 (Selected as the front cover.)
K. Fukami, K. Fukagata, K. Taira, “Super-resolution reconstruction of turbulent flows with machine learning,” Journal of Fluid Mechanics, 870, 106-120, 2019 (preprint: arXiv:1811.11328 [physics.flu-dyn]), [code]
K. Fukami, Y. Nabae, K. Kawai, K. Fukagata, “Synthetic turbulent inflow generator using machine learning,” Physical Review Fluids, 4 (064603), 2019 (preprint: arXiv:1806.08903 [physics.flu-dyn]), [Animation1] [Animation2] [code]
K. Hasegawa, K. Fukami, T. Murata, K. Fukagata, [SI] Focus on CFD32: “Prediction of the Reynolds number dependency of flow around circular cylinder using machine learning,” Nagare-Journal of Japan Society of Fluid Mechanics, 38 (2), 81-84 (invited), 2019
K. Fukami, Y. Nabae, K. Kawai, K. Fukagata, “A machine-learned turbulence generator for the channel flow,” in 2nd Pacific Rim Thermal Engineering Conference, Hawaii, USA, Dec 2019, PRTEC-24004.
M. Morimoto, K. Fukami, K. Hasegawa, T. Murata, H. Murakami, K. Fukagata, “Improvement of PIV by data augmentation based on machine learning,” in 33rd CFD symposium, Hokkaido, Japan, Nov 2019, B09-1.
T. Nakamura, K. Fukami, K. Hasegawa, T. Murata, Y. Nabae, K. Fukagata, “Machine learning of turbulent channel flows using autoencoders,” in 33rd CFD symposium, Hokkaido, Japan, Nov 2019, B10-1.
K. Fukami, K. Fukagata, K. Taira, “Machine-learned super-resolution analysis of three dimensional turbulent channel flow,” in The Japan Society of Mechanical Engineers Fluids Engineering Conference 2019, Aichi, Japan, Nov 2019, OS8-01.
K. Fukami, K. Fukagata, K. Taira, “Applications of machine-learned super-resolution algorithm for two-dimensional flow fields,” in The Japan Society of Fluid Mechanics Annual Meeting 2019, Tokyo, Japan, Sep 2019.
K. Fukami, K. Fukagata, K. Taira, “Super-resolution analysis with machine learning for low-resolution flow data,” in 11th International Symposium on Turbulence and Shear Flow Phenomena (TSFP11), Southampton, UK, July–Aug 2019, Paper 208
K. Hasegawa, K. Fukami, T. Murata, K. Fukagata, “Data-driven reduced order modeling of flows around two-dimensional bluff bodies of various shapes,” in ASME-JSME-KSME Joint Fluids Engineering Conference 2019, San Francisco, USA, July–Aug 2019, Paper 5079.
T. Murata, K. Fukami, K. Fukagata, “CNN/SINDy based reduced order modeling of un- steady flow fields,” in ASME-JSME-KSME Joint Fluids Engineering Conference 2019, San Francisco, USA, July–Aug 2019, Paper 5056.
K. Hasegawa, K. Fukami, T. Murata, K. Fukagata, “Prediction of unsteady flows using machine-learned reduced order model,” in 58th JSME Kanto Student Union Conference, Chiba, Japan, Mar 2019, Paper 1218.
T. Murata, K. Fukami, K. Fukagata, “Extraction of low dimensional modes in a flow around a circular cylinder and prediction of their temporal evolutions using machine learning,” in 32nd CFD symposium, Tokyo, Japan, Dec 2018, B04-2.
K. Hasegawa, K. Fukami, T. Murata, K. Fukagata, “Prediction of the Reynolds number dependency of flow around circular cylinder using machine learning,” in 32nd CFD symposium, Tokyo, Japan, Dec 2018, B04-1.
K. Fukami, K. Kawai, K. Fukagata, “Proposal of an inflow turbulence generator using machine learning,” in 57th JSME Kanto Student Union Conference, Tokyo, Japan, Mar 2018, Paper 204.
K. Taira, J. Tran, K. Fukami, K. Inada, D. Umehara, Y. Ono, K. Ogawa, “Aerodynamics-Informed Design Optimization of Vehicles with Machine Learning,” USPTO, provisional filing, 2024. [link]
K. Taira, K. Fukami, B. An, M. Obuchi, M. Nohmi, “Machine-learning-based flow estimation technique for fluid systems,” USPTO, provisional filing, 2022.