Zobrazeno 1 - 10
of 101
pro vyhledávání: '"Fukami, Kai"'
Autor:
Fukami, Kai, Taira, Kunihiko
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 single data file in general machine-learning applications. This study
Externí odkaz:
http://arxiv.org/abs/2409.04923
Publikováno v:
Journal of Fluid Mechanics, 992, A17, 2024
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 uses a nonlinear machine-learning technique to compress the high-dime
Externí odkaz:
http://arxiv.org/abs/2403.00263
We present a phase autoencoder that encodes the asymptotic phase of a limit-cycle oscillator, a fundamental quantity characterizing its synchronization dynamics. This autoencoder is trained in such a way that its latent variables directly represent t
Externí odkaz:
http://arxiv.org/abs/2403.06992
Publikováno v:
Journal of Fluid Mechanics, 984, R4, 2024
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 scale-invariant characteristics of turbulent flow structures. This st
Externí odkaz:
http://arxiv.org/abs/2402.17990
Large amplitude gust encounters exhibit a range of separated flow phenomena, making them difficult to characterize using the traditional tools of aerodynamics. In this work, we propose a dynamical systems approach to gust encounters, viewing the flow
Externí odkaz:
http://arxiv.org/abs/2306.15829
Autor:
Fukami, Kai, Taira, Kunihiko
Publikováno v:
Nature Communications, 14, 6480, 2023
Modern air vehicles perform a wide range of operations, including transportation, defense, surveillance, and rescue. These aircraft can fly in calm conditions but avoid operations in gusty environments, encountered in urban canyons, over mountainous
Externí odkaz:
http://arxiv.org/abs/2305.08024
Reconstruction of unsteady vortical flow fields from limited sensor measurements is challenging. We develop machine learning methods to reconstruct flow features from sparse sensor measurements during transient vortex-airfoil wake interaction using o
Externí odkaz:
http://arxiv.org/abs/2305.05147
This paper surveys machine-learning-based super-resolution reconstruction for vortical flows. Super resolution aims to find the high-resolution flow fields from low-resolution data and is generally an approach used in image reconstruction. In additio
Externí odkaz:
http://arxiv.org/abs/2301.10937
We study the compression of spatial and temporal features in fluid flow data using multimedia compression techniques. The efficacy of spatial compression techniques, including JPEG and JPEG2000 (JP2), and spatio-temporal video compression techniques,
Externí odkaz:
http://arxiv.org/abs/2301.00078
We use Gaussian stochastic weight averaging (SWAG) to assess the model-form uncertainty associated with neural-network-based function approximation relevant to fluid flows. SWAG approximates a posterior Gaussian distribution of each weight, given tra
Externí odkaz:
http://arxiv.org/abs/2109.08248