Prediction of Gust Aeroelastic performance of HALE using Graph Neural Networks

Abstract

Graph Neural Networks have been applied to learn the flight and structural dynamics of an High Altitude Long Endurance aircraft in discrete gust. The graph network methodology allows building a model for structural displacements, loads and aircraft flight dynamics leveraging on the inductive bias given by the physical connections. The results show promising capabilities in model approximation and potential for symbolic identification of aerodynamics and structural forces