# Dynamic System Identification of Underwater Vehicles Using Multi-Output Gaussian Processes

arXiv: https://arxiv.org/abs/2006.02194 · HTML: https://mlsanity.com/paper/2006.02194/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 3 Jun 2020.

## Built on by (most cited first, 2 of 2)

- [Modeling of AUV Dynamics with Limited Resources: Efficient Online Learning Using Uncertainty](https://mlsanity.com/paper/2504.04583/) (2025)
- [A Differentiable Composite Approximation Framework for Autonomous Underwater Vehicle Maneuvering Modeling from Sea-Trial Data](https://mlsanity.com/paper/2606.19711/) (2026)

## Abstract (arXiv)

Non-parametric system identification with Gaussian Processes for underwater vehicles is explored in this research with the purpose of modelling autonomous underwater vehicle (AUV) dynamics with low amount of data. Multi-output Gaussian processes and its aptitude to model the dynamic system of an underactuated AUV without losing the relationships between tied outputs is used. The simulation of a first-principles model of a Remus 100 AUV is employed to capture data for the training and validation of the multi-output Gaussian processes. The metric and required procedure to carry out multi-output Gaussian processes for AUV with 6 degrees of freedom (DoF) is also shown in this paper. Multi-output Gaussian processes are compared with the popular technique of recurrent neural network show that Multi-output Gaussian processes manage to surpass RNN for non-parametric dynamic system identification in underwater vehicles with highly coupled DoF with the added benefit of providing a measurement of confidence.