Generative AI in Ship Design

• URL: http://arxiv.org/abs/2408.16798v1
• Date: Thu, 29 Aug 2024 08:55:35 GMT
• Title: Generative AI in Ship Design
• Authors: Sahil Thakur, Navneet V Saxena, Prof Sitikantha Roy,
• Abstract summary: 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. Overall, this approach holds promise in revolutionizing ship design by exploring a broader design space and integrating multidisciplinary optimization objectives effectively.
• Score: 0.0
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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.

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