Gate-iInformer: Enhancing Long-Sequence Fuel Forecasting in Aviation via Inverted Transformers and Gating Networks

Accurately predicting aircraft fuel consumption is vital for aviation safety, operational efficiency, and resource optimization, yet existing models face key limitations. Traditional physical models rely on prior assumptions, while mainstream deep learning models use fixed architectures and time-sli...

Descripció completa

Dades bibliogràfiques

Autors principals:	Yanxiong Wu, Junqi Fu, Yu Li, Wenjing Feng, Yongshuo Zhu, Lu Li
Format:	Artigo
Idioma:	Inglês
Publicat:	MDPI AG 2025-10-01
Col·lecció:	Aerospace
Matèries:	aircraft fuel consumption prediction iInformer gating network time series prediction multi-phase adaptation
Accés en línia:	https://www.mdpi.com/2226-4310/12/10/904
Etiquetes:	Afegir etiqueta Sense etiquetes, Sigues el primer a etiquetar aquest registre!