A hybrid learning approach for the simulation of aircraft dynamical systems

Yang Yu, Houpu Yao, Yongming Liu

Research output: Chapter in Book/Report/Conference proceedingConference contribution

1 Scopus citations


The computation costs of predicting flight trajectories can be expensive or even prohibitive especially for a large number of aircrafts in the air traffic system. This study proposes the concept of hybrid learning, a novel approach based on data-driven learning and physics, as a computationally efficient method for the simulation of aircraft dynamics. The hybrid learning integrates the underlying physics of dynamical systems into learning models such as neural networks to reduce the training and simulation costs. The application of hybrid learning for simulating aircraft dynamics is demonstrated using a recently introduced physics-aware network known as the deep residual recurrent neural network (DR-RNN) on a Boeing 747-100 aircraft. The aircraft dynamics are described using a six degrees-of-freedom aircraft model. Both a long short-term memory (LSTM) network and a DR-RNN are trained using the simulated responses of the aircraft and then the trained networks are used to predict the response of aircraft under arbitrary control inputs and disturbances. The results show that the DR-RNN can accurately predict aircraft responses and has excellent extrapolation capabilities. Moreover, the DR-RNN exhibits superior computation efficiency compared with a classical numerical method, the fourth-order Runge-kutta method, highlighting its suitability in serving as surrogating models for aircraft dynamical systems.

Original languageEnglish (US)
Title of host publicationAIAA Scitech 2019 Forum
PublisherAmerican Institute of Aeronautics and Astronautics Inc, AIAA
ISBN (Print)9781624105784
StatePublished - 2019
EventAIAA Scitech Forum, 2019 - San Diego, United States
Duration: Jan 7 2019Jan 11 2019

Publication series

NameAIAA Scitech 2019 Forum


ConferenceAIAA Scitech Forum, 2019
Country/TerritoryUnited States
CitySan Diego

All Science Journal Classification (ASJC) codes

  • Aerospace Engineering


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