Generative AI in Ship Design
<|reference_start|>Generative AI in Ship Design: 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.<|reference_end|>
@article{thakur2024generative,
    title={Generative AI in Ship Design},
    author={Sahil Thakur, Navneet V Saxena, and Prof Sitikantha Roy},
    journal={arXiv preprint arXiv:2408.16798},
    year={2024},
    archivePrefix={arXiv},
    eprint={2408.16798},
    primaryClass={cs.LG cs.AI}
}