From the 1 of 6 linked papers with an AI index.
6 papers
Extracting informative vortical structures of turbulent wake-extreme vortex gust interactions with machine learning
Ryo Koshikawa, Kai Fukami
The paper applies a convolutional information‑theoretic machine‑learning method to separate informative vortical structures from residual flow in turbulent wake and vortex‑gust int…
Data-driven modeling and decomposition for nanoscale liquid-film dynamics: Application to superspreading nanofluid droplets
Kai Fukami, Eita Shoji
Understanding ultrathin liquid-film dynamics is crucial for unraveling complex interfacial phenomena, yet deriving governing equations directly from experimental observations remai…
Data-driven time-dependent bases for turbulent airfoil wake-extreme gust interactions
Shaghayegh Zamani Ashtiani, Kai Fukami
We analyze interactions between turbulent airfoil wakes and extreme gusts using a data-driven framework with time-dependent bases. The current approach represents each snapshot wit…
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…
Compact representation of transonic airfoil buffet flows with observable-augmented machine learning
Kai Fukami, Yuta Iwatani, Soju Maejima +2
Transonic buffet presents time-dependent aerodynamic characteristics associated with shock, turbulent boundary layer, and their interactions. Despite strong nonlinearities and a la…
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…