# Object Identification Under Known Dynamics: A PIRNN Approach for UAV Classification

arXiv: https://arxiv.org/abs/2509.21405 · HTML: https://mlsanity.com/paper/2509.21405/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 2 of its 25 references that are in the map.
- First posted on arXiv 24 Sep 2025.

## Builds on (most cited first, 2 of 2)

- [RAMP-Net: A Robust Adaptive MPC for Quadrotors via Physics-informed Neural Network](https://mlsanity.com/paper/2209.09025/) (2022)
- [Nonlinear System Identification of Swarm of UAVs Using Deep Learning Methods](https://mlsanity.com/paper/2311.12906/) (2023)

## Abstract (arXiv)

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.