# Generative AI in Ship Design

arXiv: https://arxiv.org/abs/2408.16798 · HTML: https://mlsanity.com/paper/2408.16798/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- First posted on arXiv 29 Aug 2024.

## Abstract (arXiv)

The process of ship design is intricate, heavily influenced by the hull form which accounts for approximately 70% of the total cost. Traditional methods rely on human-driven iterative processes based on naval architecture principles and engineering analysis. In contrast, generative AI presents a novel approach, utilizing computational algorithms rooted in machine learning and artificial intelligence to optimize ship hull design. This report outlines the systematic creation of a generative AI for this purpose, involving steps such as dataset collection, model architecture selection, training, and validation. Utilizing the "SHIP-D" dataset, consisting of 30,000 hull forms, the report adopts the Gaussian Mixture Model (GMM) as the generative model architecture. GMMs offer a statistical framework to analyze data distribution, crucial for generating innovative ship designs efficiently. Overall, this approach holds promise in revolutionizing ship design by exploring a broader design space and integrating multidisciplinary optimization objectives effectively.