Grant-in-Aid for Research Activity Start-up (Number 25K23418)
PI: Kai Fukami
Project term: 2025/7/31-2027/3/31
Budget: ¥ 2,600,000
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])
K. Taira, G. Rigas, K. Fukami, “Machine learning in fluid dynamics: A critical assessment," Physical Review Fluids, 10, 090701 (invited), 2025
K. Fukami, L. Smith, K. Taira, “Extreme vortex-gust airfoil interactions at Reynolds number 5000," Physical Review Fluids, 10, 084703, 2025
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. Taira, G. Rigas, K. Fukami, “Machine learning in fluid dynamics: A critical assessment," in the Physical Review Fluids Journal Club, online, Nov 2025. [YouTube]
[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.
[Invited] K. Fukami, “Data-driven analysis of extremely gusty aerodynamic flows,” in the JSME-KSME Joint Symposium on Computational & CAE 2025 at Gwangju Institute of Science and Technology, Gwangju, Korea, Aug 2025.