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...

ver descrição completa

Detalhes bibliográficos

Principais autores:	Yanxiong Wu, Junqi Fu, Yu Li, Wenjing Feng, Yongshuo Zhu, Lu Li
Formato:	Artigo
Idioma:	Inglês
Publicado em:	MDPI AG 2025-10-01
coleção:	Aerospace
Assuntos:	aircraft fuel consumption prediction iInformer gating network time series prediction multi-phase adaptation
Acesso em linha:	https://www.mdpi.com/2226-4310/12/10/904
Tags:	Adicionar Tag Sem tags, seja o primeiro a adicionar uma tag!