Generative AI in Ship Design

TL;DR

This paper explores the use of generative AI, specifically Gaussian Mixture Models, to optimize ship hull design by analyzing a large dataset, aiming to revolutionize traditional iterative design methods.

Contribution

It introduces a systematic approach to applying generative AI with GMMs for ship hull design, leveraging a large dataset to enhance design exploration and optimization.

Findings

Utilized the SHIP-D dataset with 30,000 hull forms.

Implemented GMM as the generative model architecture.

Demonstrated potential for broader design space exploration.

Abstract

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.