4 papers
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…
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…
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…
Single-snapshot machine learning for super-resolution of turbulence
Kai Fukami, Kunihiko Taira
Modern machine-learning techniques are generally considered data-hungry. However, this may not be the case for turbulence as each of its snapshots can hold more information than a…