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
S. Zamani Ashtiani, K. Fukami, “Data-driven time-dependent bases for turbulent airfoil wake-extreme gust interactions," AIAA Journal, accepted, 2026 (preprint, arXiv:2512.09523 [physics.comp-ph])
K. Fukami, E. Shoji, “Data-driven modeling and decomposition for nanoscale liquid-film dynamics: Application to superspreading nanofluid droplets," European Journal of Mechanics - B/Fluids, 121, 204596, 2027 (preprint, arXiv:2601.01776 [physics.flu-dyn])
R. Koshikawa, R. Araki, Q. Liu, K. Fukami, “Convolutional causal learning for aerodynamic flows," Journal of Fluid Mechanics, 1037, A6, 2026 (preprint, arXiv:2601.19104 [physics.flu-dyn])
[Invited] R. Koshikawa, R. Araki, Q. Liu, K. Fukami, “Transient causal modal analysis of aerodynamic flows with information-theoretic convolutional learning," in SIAM Conference on Annual Meeting (AN26), Cleveland, Ohio, USA, Jul 2026.
R. Koshikawa, R. Araki, Q. Liu, K. Fukami, “Causal relationship between vortical structures and lift response identified via information-theoretic machine learning," in the 44th meeting of the Japan Society for Aeronautical and Space Sciences Aerodynamics Division, Tottori, Japan, June 2026
S. Zamani Ashtiani, K. Fukami, “Data-driven time-dependent modal analysis for extreme aerodynamic flows," in the 44th meeting of the Japan Society for Aeronautical and Space Sciences Aerodynamics Division, Tottori, Japan, June 2026.
T. Hashimoto, T. Tsukahara, R. Araki, “Estimating data-driven equations based on causal decomposition," in the 42nd Workshop on Turbulence Control, Yokohama, Japan, June 2026.
[Invited] K. Fukami, “Taming unsteady flows with nonlinear machine learning: An observable-augmented encoder-decoder perspective,” in the seminar at the Department of Mechanical Engineering, University College London, UK, June 2026. [Flyer]
S. Zamani Ashtiani, K. Fukami, “Reduced-order modeling of unsteady aerodynamic flows via evolving bases and latent representations," in the student seminar session between Tohoku University and University College London, UK, June 2026.
R. Koshikawa, K. Fukami, “Revealing the causal relationship of unsteady flows with information-theoretic machine learning," in the student seminar session between Tohoku University and University College London, UK, June 2026.
[Invited] K. Fukami, “Let us machine-learn aerodynamics!,” in the lecture series by NISTEP Researchers 2025, MEXT, online, June 2026.
[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.
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.
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