2 citations · 2 across the 1 of their papers we have counts for
2 papers
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…
physics.flu-dyn2025
Information-theoretic machine learning for time-varying mode decomposition of separated aerodynamic flows
Kai Fukami, Ryo Araki
We perform an information-theoretic mode decomposition for separated aerodynamic flows. The current data-driven approach based on a neural network referred to as deep sigmoidal flo…