Data-driven modeling of transonic nonlinear flutter via convolutional neural network autoencoder

This paper proposes a novel data-driven aeroelastic modeling method based on the autoencoder (AE) and the nonlinear state-space identification. This method allows high-dimensional flow field nonlinear features to be characterized with high accuracy by lower-dimensional latent vectors, which facilita...

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Vydáno v:	Nonlinear dynamics Ročník 113; číslo 13; s. 15741 - 15760
Hlavní autoři:	Peng, Zhijie, Yao, Xiangjie, Liu, Haojie, Huang, Rui
Médium:	Journal Article
Jazyk:	angličtina
Vydáno:	Dordrecht Springer Netherlands 01.07.2025 Springer Nature B.V
Témata:	Aerodynamic forces Aerodynamics Aeroelasticity Applications of Nonlinear Dynamics and Chaos Theory Artificial neural networks Classical Mechanics Control Coupling Dynamical Systems Flow mapping Limit cycle oscillations Neural networks Physics Physics and Astronomy Statistical Physics and Dynamical Systems Structural models Transonic flow Transonic flutter Unsteady flow Vibration Data-driven model Unsteady flow Autoencoder Transonic aeroelastic Nonlinear state-space
ISSN:	0924-090X, 1573-269X
On-line přístup:	Získat plný text
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