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physics.flu-dyn2025
Machine learning in fluid dynamics: A critical assessment
Kunihiko Taira, Georgios Rigas, Kai Fukami
The fluid dynamics community has increasingly adopted machine learning to analyze, model, predict, and control a wide range of flows. These methods offer powerful computational cap…
nlin.AO2025★ 5 cited
Phase autoencoder for rapid data-driven synchronization of rhythmic spatiotemporal patterns
Koichiro Yawata, Ryo Sakuma, Kai Fukami +2
We present a machine-learning method for data-driven synchronization of rhythmic spatiotemporal patterns in reaction-diffusion systems. Based on the phase autoencoder [Yawata {\it…
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…