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…
Optimally time-dependent modes of vortex gust-airfoil interactions
Yonghong Zhong, Alireza Amiri-Margavi, Hessam Babaee +1
We find the optimally time-dependent (OTD) orthogonal modes about a time-varying flow generated by a strong gust vortex impacting a NACA 0012 airfoil. This OTD analysis reveals the…
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…