2 citations · 2 across the 2 of their papers we have counts for
2 papers
physics.flu-dyn2026
Extracting informative vortical structures of turbulent wake-extreme vortex gust interactions with machine learning
Ryo Koshikawa, Kai Fukami
This study considers extracting causally important vortical structures from the extreme vortex gust-airfoil interaction at a chord-based Reynolds number of . This extraction…
physics.flu-dyn2026★ 2 cited
Convolutional causal learning for aerodynamic flows
Ryo Koshikawa, Ryo Araki, Qiong Liu +1
This study aims to capture aerodynamic causality from snapshot data with a time-varying mode decomposition technique referred to as information-theoretic machine learning. The curr…