Extracting causal relationships in unsteady aerodynamic flows with information-theoretic machine learning

• Grant-in-Aid for Scientific Research B (Number 26K01129)

• PI: Kai Fukami, Co-investigator: Ryo Araki

• Project term: 2026/4/1-2029/3/31

• Budget: ¥ 18,330,000

Publications

FY2026

1. R. Koshikawa, R. Araki, Q. Liu, K. Fukami, “Convolutional causal learning for aerodynamic flows," Journal of Fluid Mechanics, accepted, 2026 (preprint, arXiv:2601.19104 [physics.flu-dyn])

Conference Talks

FY2026

1. [Invited] K. Fukami, “Taming highly unsteady flows with nonlinear machine learning: progress and outlook,” in the seminar at the Department of Mechanical and Aerospace Engineering, Institut Teknologi Bandung, Bandung, Indonesia, May 2026.

2. E. Shoji, K. Fukami, “Extraction of physical factors in nanofluid superspreading wetting via phase-shifting ellipsometry and sparse modeling," in the 68th Theoretical and Applied Mechanics Conference, Tokyo, Japan, May 2026.

3. J. Jang, J. Kang, K. Fukami, J. Jeon, S. Lee, “Energy Dissipation Model: Physics-oriented predictive model via progressive frequency refinement," in the 2026 Spring Meeting of the Korean Society for Computational Fluid Engineering, Jecheon, South Korea, April 2026