Predicting Ship Responses in Different Seaways using a Generalizable Force Correcting Machine Learning Method

TL;DR

This paper introduces a hybrid machine learning approach that corrects low-fidelity physics models to predict ship responses in various seaways, demonstrating improved accuracy and generalizability with limited training data.

Contribution

The study presents a novel hybrid ML method that enhances prediction accuracy and generalizability for ship response modeling, especially with small datasets, by integrating physics-based corrections.

Findings

Hybrid method outperforms linear models and LSTM in accuracy.

Improved generalizability in predicting responses in unseen wave conditions.

Effective with small training datasets, reducing reliance on expensive high-fidelity data.

Abstract

A machine learning (ML) method is generalizable if it can make predictions on inputs which differ from the training dataset. For predictions of wave-induced ship responses, generalizability is an important consideration if ML methods are to be useful in design evaluations. Furthermore, the size of the training dataset has a significant impact on the practicality of a method, especially when training data is generated using high-fidelity numerical tools which are expensive. This paper considers a hybrid machine learning method which corrects the force in a low-fidelity equation of motion. The method is applied to two different case studies: the nonlinear responses of a Duffing equation subject to irregular excitation, and high-fidelity heave and pitch response data of a Fast Displacement Ship (FDS) in head seas. The generalizability of the method is determined in both cases by making predictions of the response in irregular wave conditions that differ from those in the training dataset. The influence that low-fidelity physics-based terms in the hybrid model have on generalizability is also investigated. The predictions are compared to two benchmarks: a linear physics-based model and a data-driven LSTM model. It is found that the hybrid method offers an improvement in prediction accuracy and generalizability when trained on a small dataset.