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...

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Bibliographic Details

Published in:	Aerospace
Main Authors:	Yanxiong Wu, Junqi Fu, Yu Li, Wenjing Feng, Yongshuo Zhu, Lu Li
Format:	Article
Language:	English
Published:	MDPI AG 2025-10-01
Subjects:	aircraft fuel consumption prediction iInformer gating network time series prediction multi-phase adaptation
Online Access:	https://www.mdpi.com/2226-4310/12/10/904