21 citations · 29 across the 3 of their papers we have counts for
3 papers
physics.flu-dyn2025★ 21 cited
Compressing fluid flows with nonlinear machine learning: mode decomposition, latent modeling, and flow control
Koji Fukagata, Kai Fukami
An autoencoder is a self-supervised machine-learning network trained to output a quantity identical to the input. Owing to its structure possessing a bottleneck with a lower dimens…
physics.flu-dyn2023
Machine learning based dimension reduction for a stable modeling of periodic flow phenomena
Hiroshi Omichi, Takeru Ishize, Koji Fukagata
In designing efficient feedback control laws for fluid flow, the modern control theory can serve as a powerful tool if the model can be represented by a linear ordinary differentia…
physics.flu-dyn2023★ 8 cited
Flow control by a hybrid use of machine learning and control theory
Takeru Ishize, Hiroshi Omichi, Koji Fukagata
Flow control has a great potential to contribute to the sustainable society through mitigation of environmental burden. However, high dimensional and nonlinear nature of fluid flow…