# An application of machine learning to the motion response prediction of floating assets

arXiv: https://arxiv.org/abs/2506.15713 · HTML: https://mlsanity.com/paper/2506.15713/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 4 of its 26 references that are in the map.
- First posted on arXiv 31 May 2025.

## Builds on (most cited first, 4 of 4)

- [XGBoost: A Scalable Tree Boosting System](https://mlsanity.com/paper/1603.02754/) (2016)
- [Machine Learning for Fluid Mechanics](https://mlsanity.com/paper/1905.11075/) (2019)
- [Neural General Circulation Models for Weather and Climate](https://mlsanity.com/paper/2311.07222/) (2023)
- [Deep Generative Models in Engineering Design: A Review](https://mlsanity.com/paper/2110.10863/) (2021)

## Abstract (arXiv)

The real-time prediction of floating offshore asset behavior under stochastic metocean conditions remains a significant challenge in offshore engineering. While traditional empirical and frequency-domain methods work well in benign conditions, they struggle with both extreme sea states and nonlinear responses. This study presents a supervised machine learning approach using multivariate regression to predict the nonlinear motion response of a turret-moored vessel in 400 m water depth. We developed a machine learning workflow combining a gradient-boosted ensemble method with a custom passive weathervaning solver, trained on approximately 106 samples spanning 100 features. The model achieved mean prediction errors of less than 5% for critical mooring parameters and vessel heading accuracy to within 2.5 degrees across diverse metocean conditions, significantly outperforming traditional frequency-domain methods. The framework has been successfully deployed on an operational facility, demonstrating its efficacy for real-time vessel monitoring and operational decision-making in offshore environments.