C-ShipGen: A Conditional Guided Diffusion Model for Parametric Ship Hull Design
<|reference_start|>C-ShipGen: A Conditional Guided Diffusion Model for Parametric Ship Hull Design: Ship design is a complex design process that may take a team of naval architects many years to complete. Improving the ship design process can lead to significant cost savings, while still delivering high-quality designs to customers. A new technology for ship hull design is diffusion models, a type of generative artificial intelligence. Prior work with diffusion models for ship hull design created high-quality ship hulls with reduced drag and larger displaced volumes. However, the work could not generate hulls that meet specific design constraints. This paper proposes a conditional diffusion model that generates hull designs given specific constraints, such as the desired principal dimensions of the hull. In addition, this diffusion model leverages the gradients from a total resistance regression model to create low-resistance designs. Five design test cases compared the diffusion model to a design optimization algorithm to create hull designs with low resistance. In all five test cases, the diffusion model was shown to create diverse designs with a total resistance less than the optimized hull, having resistance reductions over 25%. The diffusion model also generated these designs without retraining. This work can significantly reduce the design cycle time of ships by creating high-quality hulls that meet user requirements with a data-driven approach.<|reference_end|>
@article{bagazinski2024c-shipgen:,
    title={C-ShipGen: A Conditional Guided Diffusion Model for Parametric Ship Hull
  Design},
    author={Noah J. Bagazinski, Faez Ahmed},
    journal={arXiv preprint arXiv:2407.03333},
    year={2024},
    archivePrefix={arXiv},
    eprint={2407.03333},
    primaryClass={eess.SY cs.CE cs.LG cs.SY}
}