Generative AI in Ship Design
This is an incremental application of existing generative AI methods to the domain-specific problem of ship design, potentially reducing costs and improving efficiency for naval architects.
The paper tackled ship hull design optimization by developing a generative AI using a Gaussian Mixture Model on the SHIP-D dataset of 30,000 hull forms, resulting in a method that explores a broader design space and integrates multidisciplinary objectives.
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.