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[2602.18403] Scientific Knowledge-Guided Machine Learning for Vessel Power Prediction: A Comparative Study
Summary
This study presents a hybrid modeling framework that combines scientific knowledge with machine learning to improve vessel power prediction, demonstrating superior performance in sparse data regions.
Why It Matters
Accurate vessel power prediction is crucial for optimizing performance and fuel efficiency while adhering to emission regulations. This research integrates physics-based insights with machine learning, offering a more reliable approach that can enhance operational efficiency in maritime applications.
Key Takeaways
- Hybrid model integrates physics-based knowledge with machine learning for better predictions.
- Outperforms traditional data-driven models, especially in sparse data scenarios.
- Improves generalization and consistency with physical laws governing vessel power.
- Practical applications include weather routing and energy efficiency planning.
- Demonstrates the importance of combining scientific principles with AI techniques.
Computer Science > Machine Learning arXiv:2602.18403 (cs) [Submitted on 20 Feb 2026] Title:Scientific Knowledge-Guided Machine Learning for Vessel Power Prediction: A Comparative Study Authors:Orfeas Bourchas, George Papalambrou View a PDF of the paper titled Scientific Knowledge-Guided Machine Learning for Vessel Power Prediction: A Comparative Study, by Orfeas Bourchas and 1 other authors View PDF HTML (experimental) Abstract:Accurate prediction of main engine power is essential for vessel performance optimization, fuel efficiency, and compliance with emission regulations. Conventional machine learning approaches, such as Support Vector Machines, variants of Artificial Neural Networks (ANNs), and tree-based methods like Random Forests, Extra Tree Regressors, and XGBoost, can capture nonlinearities but often struggle to respect the fundamental propeller law relationship between power and speed, resulting in poor extrapolation outside the training envelope. This study introduces a hybrid modeling framework that integrates physics-based knowledge from sea trials with data-driven residual learning. The baseline component, derived from calm-water power curves of the form $P = cV^n$, captures the dominant power-speed dependence, while another, nonlinear, regressor is then trained to predict the residual power, representing deviations caused by environmental and operational conditions. By constraining the machine learning task to residual corrections, the hybrid model simplifie...