Physics-Informed Machine Learning for Vessel Shaft Power and Fuel Consumption Prediction: Interpretable KAN-based Approach

TL;DR

This paper presents a hybrid physics-informed machine learning model called PI-KAN that accurately predicts vessel shaft power and fuel consumption while maintaining interpretability and physical consistency, outperforming traditional methods.

Contribution

The paper introduces PI-KAN, a novel hybrid model combining interpretable features with physics-informed loss, improving prediction accuracy and interpretability in maritime vessel performance modeling.

Findings

PI-KAN outperforms polynomial and neural network baselines in accuracy.

The model maintains physically consistent behavior across vessels.

Interpretability analysis rediscovered domain-specific dependencies.

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, while maintaining physically consistent behavior. Interpretability analysis reveals rediscovery of domain-consistent dependencies, such as cubic-like speed-power relationships and cosine-like wave and wind effects. These results demonstrate that PI-KAN achieves both predictive accuracy and interpretability, offering a robust tool for vessel performance monitoring and decision support in operational settings.