Year: 2025
Type: review
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 R2 = 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.
DOI: 10.3390/jmse13091719
Institutions: University of Strathclyde, BAE Systems (United Kingdom)
Language: English
Subfield: Ocean Engineering
Field: Engineering
Domain: Physical Sciences
Open Access status: gold
Estimated APC paid: $3,137
OpenAlex ID: W4414017687