AI-based optimization techniques for hydrodynamic and structural design in ships : a review

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Htein, Nay Min and Louvros, Panagiotis and Stefanou, Evangelos and Aung, Myo Zin and Hifi, Nabile and Boulougouris, Evangelos (2025) AI-based optimization techniques for hydrodynamic and structural design in ships : a review. Journal of Marine Science and Engineering, 13 (9). 1719. ISSN 2077-1312 (https://doi.org/10.3390/jmse13091719)

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Abstract

Artificial Intelligence (AI) is increasingly integrated into ship design workflows, offering enhanced capabilities for hydrodynamic and structural optimization. This review focuses on AI-based methods applied to key design tasks such as hull resistance prediction, structural weight reduction, and performance-driven form optimization. Techniques examined include deep neural networks (DNNs), support vector machines (SVMs), tree-based ensemble models, genetic algorithms (GAs), and surrogate modeling approaches. Comparative analyses from the literature indicate that ensemble tree methods, such as XGBoost, have achieved predictive accuracies up to 2 = 0.995 in speed–power modeling, marginally surpassing DNN performance, while GA-based structural optimization studies have reported weight reductions exceeding 10%. The findings confirm that no single method is universally superior; rather, effectiveness depends on the problem definition, data quality, and computational resources available. Hybrid strategies that combine physics-based modeling with data-driven learning have demonstrated improved generalization, reduced data requirements, and enhanced interpretability. Practical challenges remain, including limited access to open high-fidelity datasets, the computational demands of complex models, and balancing predictive accuracy with explainability. The review concludes that AI should be employed as a complementary toolkit to augment human expertise, with method selection guided by design objectives, constraints, and integration within the broader ship design process.

ORCID iDs

Htein, Nay Min ORCID logoORCID: https://orcid.org/0009-0007-9534-4509, Louvros, Panagiotis ORCID logoORCID: https://orcid.org/0000-0001-8623-0680, Stefanou, Evangelos ORCID logoORCID: https://orcid.org/0009-0005-7586-4676, Aung, Myo Zin ORCID logoORCID: https://orcid.org/0000-0001-6370-0029, Hifi, Nabile and Boulougouris, Evangelos ORCID logoORCID: https://orcid.org/0000-0001-5730-007X;
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