{"ID":2866710,"CreatedAt":"2026-06-01T04:54:23.091178241Z","UpdatedAt":"2026-06-01T04:54:23.091178241Z","DeletedAt":null,"paper_url":"https://arxiv.org/abs/2509.21405","arxiv_id":"2509.21405","title":"Object Identification Under Known Dynamics: A PIRNN Approach for UAV Classification","abstract":"This work addresses object identification under known dynamics in unmanned aerial vehicle applications, where learning and classification are combined through a physics-informed residual neural network. The proposed framework leverages physics-informed learning for state mapping and state-derivative prediction, while a softmax layer enables multi-class confidence estimation. Quadcopter, fixed-wing, and helicopter aerial vehicles are considered as case studies. The results demonstrate high classification accuracy with reduced training time, offering a promising solution for system identification problems in domains where the underlying dynamics are well understood.","short_abstract":"This work addresses object identification under known dynamics in unmanned aerial vehicle applications, where learning and classification are combined through a physics-informed residual neural network. The proposed framework leverages physics-informed learning for state mapping and state-derivative prediction, while a...","url_abs":"https://arxiv.org/abs/2509.21405","url_pdf":"https://arxiv.org/pdf/2509.21405v1","authors":"[\"Nyi Nyi Aung\",\"Neil Muralles\",\"Adrian Stein\"]","published":"2025-09-24T17:09:21Z","proceeding":"cs.LG","tasks":"[\"cs.LG\",\"cs.RO\"]","methods":"[]","has_code":false}