Establishing a low-rank manifold village toward mathematic prediction and control of unsteady flows
Establishing a low-rank manifold village toward mathematic prediction and control of unsteady flows
JST PRESTO (Number JPMJPR25KA)
PI: Kai Fukami
Project term: 2025/10/1-2029/3/31
Budget: ¥ 39,000,000
K. Yawata, R. Sakuma, K. Fukami, K. Taira, H. Nakao, “Phase autoencoder for rapid data-driven synchronization of rhythmic spatiotemporal patterns," Physical Review E, 112, 064211, 2025 (preprint, arXiv:2506.12777 [nlin.AO])
K. Fukami, Y. Iwatani, S. Maejima, H. Asada, S. Kawai, “Compact representation of transonic airfoil buffet flows with observable-augmented machine learning,” Journal of Fluid Mechanics, 1021, A39, 2025 (preprint, arXiv:2509.17306 [physics.flu-dyn])
S. Zamani Ashtiani, K. Fukami, “Data-driven time-dependent modal analysis for extreme vortex-gust airfoil interactions," in the 39th CFD symposium, Kitakyushu, Japan, Dec 2025.
R. Koshikawa, R. Araki, Q. Liu, K. Fukami, “Transient causal modal analysis of unsteady aerodynamics: A global learning approach," in the 39th CFD symposium, Kitakyushu, Japan, Dec 2025.
K. Fukami, R. Koshikawa, R. Araki, “Transient causal modal analysis of unsteady aerodynamics: A local learning approach," in the 39th CFD symposium, Kitakyushu, Japan, Dec 2025.
K. Fukami, “Data-driven modeling and control of unsteady flows on a low-rank manifold," in the JST PRESTO meeting for the research area of “Mathematical Sciences for the Future", Tokyo, Japan, Nov 2025
H. Shin, K. Fukami, S. Lee, D. You, S. Bagheri, “Interpreting and modeling surface roughness effects via drag-augmented manifold learning," in the 78th Annual Meeting of the APS Division of Fluid Dynamics, Houston, Texas, USA, Nov 2025.
K. Fukami, R. Araki, “Extracting time-varying causal modes of aerodynamic flows with information-theoretic machine learning," in the 78th Annual Meeting of the APS Division of Fluid Dynamics, Houston, Texas, USA, Nov 2025. (chosen as an interact session talk on Machine Learning for Fluid Mechanics)
K. Fukami, R. Araki, “Machine-learning-based informative mode analysis for airfoil wakes,” in the 22nd International Conference on Flow Dynamics, Miyagi, Japan, Nov 2025.
[Invited] K. Fukami, “Data-oriented modeling and control of unsteady flows: generalized super-resolution and manifold learning,” in the Biofluid Workshop on data processing for biofluid dynamics at Research Institute for Mathematical Sciences, Kyoto University, Kyoto, Japan, Oct 2025.
R. Araki, K. Fukami, “Extracting time-varying causal modes with information-theoretic machine learning for unsteady separated airfoil wakes,” in the 63rd Aircraft Symposium, Okinawa, Japan, Oct 2025.