Orbital Motion

Orbital motion research focuses on understanding and predicting the movement of objects in space, encompassing celestial bodies and artificial satellites. Current research employs machine learning techniques, including neural networks (e.g., convolutional, recurrent, and graph neural networks), to analyze observational data, identify governing equations, and improve control systems for spacecraft navigation and rendezvous. These advancements are crucial for optimizing satellite operations, discovering exoplanets, and enhancing our understanding of complex dynamical systems like protoplanetary disks and hierarchical star systems. The resulting improvements in modeling accuracy and computational efficiency have significant implications for various fields, including aerospace engineering, astronomy, and plasma physics.