43 citations · 120 across the 7 of their papers we have counts for
7 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…
Data-driven transient lift attenuation for extreme vortex gust-airfoil interactions
Kai Fukami, Hiroya Nakao, Kunihiko Taira
We present a data-driven feedforward control to attenuate large transient lift experienced by an airfoil disturbed by an extreme level of discrete vortex gust. The current analysis…
Data-driven nonlinear turbulent flow scaling with Buckingham Pi variables
Kai Fukami, Susumu Goto, Kunihiko Taira
Nonlinear machine learning for turbulent flows can exhibit robust performance even outside the range of training data. This is achieved when machine-learning models can accommodate…
Phase autoencoder for limit-cycle oscillators
Koichiro Yawata, Kai Fukami, Kunihiko Taira +1
We present a phase autoencoder that encodes the asymptotic phase of a limit-cycle oscillator, a fundamental quantity characterizing its synchronization dynamics. This autoencoder i…