A prior information informed learning architecture for flying trajectory prediction

ArXi:2603.06863v1 Announce Type: new Trajectory prediction for flying objects is critical in domains ranging from sports analytics to aerospace. However, traditional methods struggle with complex physical modeling, computational inefficiencies, and high hardware demands, often neglecting critical trajectory events like landing points. This paper