# Nonlinear System Identification of Swarm of UAVs Using Deep Learning Methods

arXiv: https://arxiv.org/abs/2311.12906 · HTML: https://mlsanity.com/paper/2311.12906/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 5 of its 16 references that are in the map.
- First posted on arXiv 21 Nov 2023.

## Builds on (most cited first, 5 of 5)

- [Deep learning for time series classification: a review](https://mlsanity.com/paper/1809.04356/) (2018)
- [Physics Informed Deep Learning (Part I): Data-driven Solutions of Nonlinear Partial Differential Equations](https://mlsanity.com/paper/1711.10561/) (2017)
- [Multistep Neural Networks for Data-driven Discovery of Nonlinear Dynamical Systems](https://mlsanity.com/paper/1801.01236/) (2018)
- [Hybrid Forecasting of Chaotic Processes: Using Machine Learning in Conjunction with a Knowledge-Based Model](https://mlsanity.com/paper/1803.04779/) (2018)
- [How Entropic Regression Beats the Outliers Problem in Nonlinear System Identification](https://mlsanity.com/paper/1905.08061/) (2019)

## Built on by (most cited first, 1 of 1)

- [Object Identification Under Known Dynamics: A PIRNN Approach for UAV Classification](https://mlsanity.com/paper/2509.21405/) (2025)

## Abstract (arXiv)

This study designs and evaluates multiple nonlinear system identification techniques for modeling the UAV swarm system in planar space. learning methods such as RNNs, CNNs, and Neural ODE are explored and compared. The objective is to forecast future swarm trajectories by accurately approximating the nonlinear dynamics of the swarm model. The modeling process is performed using both transient and steady-state data from swarm simulations. Results show that the combination of Neural ODE with a well-trained model using transient data is robust for varying initial conditions and outperforms other learning methods in accurately predicting swarm stability.