Designing super-quick control strategies of unsteady flows based on nonlinear phase-amplitude machine learning

• 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

Publications

1. K. Taira, G. Rigas, K. Fukami, “Machine learning in fluid dynamics: A critical assessment," Physical Review Fluids (invited), accepted, 2025

2. K. Fukami, L. Smith, K. Taira, “Extreme vortex-gust airfoil interactions at Reynolds number 5000," Physical Review Fluids, 10, 084703, 2025

Conference Talks

1. [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.