A Physics-Informed Neural Network Method-based Prediction of Ship Rolling Motion

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To enhance the prediction accuracy and robustness of ship rolling motion while addressing the limited generalization capability of traditional identification methods under complex sea conditions, a high-fidelity model for ship rolling motion based on Physics-Informed Neural Networks (PINN) is proposed. By constructing a time-series input-output relationship for ship rolling motion, incorporating its physical mechanisms, and employing a multi-layer neural network to model ship motion, the PINN-based rolling motion identification model is ultimately trained using a Mean Squared Error (MSE) loss function. Ship motion data collected from the South China Sea via inertial motion sensors is utilized for comparative experiments against a Support Vector Machine (SVM) algorithm across four dimensions: generalization validation, time scales, data volume, and sampling intervals. The results demonstrate that PINN-based significantly outperforms SVM, reducing the Root Mean Square Error (RMSE) by 36.98% in long-term predictions while improving the coefficient of determination (R²) by over 60%. Under limited training data conditions, PINN-based achieves an 87.1% reduction in RMSE and a 98.5% increase in R². These findings indicate that PINN-based enables high-fidelity identification of ship rolling motion models while exhibiting superior robustness and applicability.

Recommended citation: Fang, X., Zhu, M., Wang, Z. H., Guo, H. T., & Tian, K. (2025). A Physics-Informed Neural Network Method-based Prediction of Ship Rolling Motion. IFAC-PapersOnLine, 59(22), 878-883.

Recommended citation: Fang, X., Zhu, M., Wang, Z. H., Guo, H. T., & Tian, K. (2025). A Physics-Informed Neural Network Method-based Prediction of Ship Rolling Motion. IFAC-PapersOnLine, 59(22), 878-883.
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