Fusing deep learning features for parameter identification of a stochastic airfoil system-Reference-Cited by-同舟云学术

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Fusing deep learning features for parameter identification of a stochastic airfoil system

Published:2024-08-27 Issue: Volume: Page:
ISSN:0924-090X
Container-title:Nonlinear Dynamics
language:en
Short-container-title:Nonlinear Dyn

Author:

Feng Jing,Wang Xiaolong,Liu Qi,Xu Yong,Kurths Jürgen

Funder

the National Natural Science Foundation of China

Scientific Research Program Funded by Shaanxi Provincial Education Department

Natural Science Basic Research Program of Shaanxi, China

the Key International (Regional) Joint Research Program of the NSF of China

the NSF of China

Publisher

Springer Science and Business Media LLC

Link

https://link.springer.com/content/pdf/10.1007/s11071-024-10152-6.pdf

Reference71 articles.

1. Liu, Q., Xu, Y., Kurths, J., Liu, X.: Complex nonlinear dynamics and vibration suppression of conceptual airfoil models: a state-of-the-art overview. Chaos 32, 062101 (2022)

2. Liu, Q., Xu, Y., Li, Y.: Complex dynamics of a conceptual airfoil structure with consideration of extreme flight conditions. Nonlinear Dyn. 111(16), 14991–15010 (2023)

3. Ma, J., Liu, Q., Xu, Y., Kurths, J.: Early warning of noise-induced catastrophic high-amplitude oscillations in an airfoil model. Chaos 32(3), 033119 (2022)

4. Tripathi, D., Vishal, S., Bose, C., Venkatramani, J.: Stall-induced fatigue damage in nonlinear aeroelastic systems under stochastic inflow: numerical and experimental analyses. Int. J. Non Linear Mech. 142, 104003 (2022)

5. Lee, B.H.K., Price, S.J., Wong, Y.S.: Nonlinear aeroelastic analysis of airfoils: bifurcation and chaos. Prog. Aerosp. Sci. 35(3), 205–334 (1999)

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