![[2602.22055] Physics-Informed Machine Learning for Vessel Shaft Power and Fuel Consumption Prediction: Interpretable KAN-based Approach](https://arxiv.org/static/browse/0.3.4/images/arxiv-logo-fb.png){kind=logo}
[2602.22055] Physics-Informed Machine Learning for Vessel Shaft Power and Fuel Consumption Prediction: Interpretable KAN-based Approach
Summary
This article presents a novel Physics-Informed Kolmogorov-Arnold Network (PI-KAN) for predicting vessel shaft power and fuel consumption, combining interpretability with predictive accuracy.
Why It Matters
The study addresses the critical need for accurate and interpretable models in maritime operations, balancing the strengths of physics-based and data-driven approaches. By improving prediction methods, it enhances operational efficiency and sustainability in the shipping industry, which is vital for reducing environmental impact.
Key Takeaways
- PI-KAN integrates physics-informed methods with machine learning for better predictions.
- The model outperforms traditional methods in accuracy and interpretability.
- It reveals important domain-consistent relationships in vessel performance data.
- Operational data from five cargo vessels validates the model's effectiveness.
- The approach supports decision-making in maritime operations.
Computer Science > Machine Learning arXiv:2602.22055 (cs) [Submitted on 25 Feb 2026] Title:Physics-Informed Machine Learning for Vessel Shaft Power and Fuel Consumption Prediction: Interpretable KAN-based Approach Authors:Hamza Haruna Mohammed, Dusica Marijan, Arnbjørn Maressa View a PDF of the paper titled Physics-Informed Machine Learning for Vessel Shaft Power and Fuel Consumption Prediction: Interpretable KAN-based Approach, by Hamza Haruna Mohammed and 2 other authors View PDF HTML (experimental) Abstract:Accurate prediction of shaft rotational speed, shaft power, and fuel consumption is crucial for enhancing operational efficiency and sustainability in maritime transportation. Conventional physics-based models provide interpretability but struggle with real-world variability, while purely data-driven approaches achieve accuracy at the expense of physical plausibility. This paper introduces a Physics-Informed Kolmogorov-Arnold Network (PI-KAN), a hybrid method that integrates interpretable univariate feature transformations with a physics-informed loss function and a leakage-free chained prediction pipeline. Using operational and environmental data from five cargo vessels, PI-KAN consistently outperforms the traditional polynomial method and neural network baselines. The model achieves the lowest mean absolute error (MAE) and root mean squared error (RMSE), and the highest coefficient of determination (R^2) for shaft power and fuel consumption across all vessels, whil...