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Ship design is a years-long process that requires balancing complex designtrade-offs to create a ship that is efficient and effective. Finding new waysto improve the ship design process can lead to significant cost savings forship building and operation. One promising technology is generative artificialintelligence, which has been shown to reduce design cycle time and createnovel, high-performing designs. In literature review, generative artificialintelligence has been shown to generate ship hulls; however, ship design isparticularly difficult as the hull of a ship requires the consideration of manyobjectives. This paper presents a study on the generation of parametric shiphull designs using a parametric diffusion model that considers multipleobjectives and constraints for the hulls. This denoising diffusionprobabilistic model (DDPM) generates the tabular parametric design vectors of aship hull for evaluation. In addition to a tabular DDPM, this paper detailsadding guidance to improve the quality of generated ship hull designs. Byleveraging classifier guidance, the DDPM produced feasible parametric shiphulls that maintain the coverage of the initial training dataset of ship hullswith a 99.5% rate, a 149x improvement over random sampling of the design vectorparameters across the design space. Parametric ship hulls produced withperformance guidance saw an average of 91.4% reduction in wave dragcoefficients and an average of a 47.9x relative increase in the total displacedvolume of the hulls compared to the mean performance of the hulls in thetraining dataset. The use of a DDPM to generate parametric ship hulls canreduce design time by generating high-performing hull designs for futureanalysis. These generated hulls have low drag and high volume, which can reducethe cost of operating a ship and increase its potential to generate revenue.

ShipGen: A Diffusion Model for Parametric Ship Hull Generation with Multiple Objectives and Constraints