Representation-Aware Generative Modeling for Early-Stage Ship Design

Early-stage ship design demands efficient exploration of diverse hull forms under tight geometric and performance constraints. Existing data-driven approaches often rely on a single geometric representation, limiting their ability to capture both global proportions and local shape detail. This paper presents a representation-aware generative framework that learns a shared latent encoding of hull geometry from multiple modalities: surface point clouds, waterline sections, and buttock curves. A conditional multimodal autoencoder embeds these complementary descriptors into a unified latent space, enforcing cross-modal consistency and preserving geometric fidelity. To enable the generation of novel hull forms, a latent diffusion model is trained to sample hull representations conditioned on high-level design parameters, such as principal dimensions and form coefficients. The proposed approach produces watertight and geometrically coherent hull forms that align with user-specified design parameters. Experiments on a synthetic hull dataset demonstrate improved geometric coherence and controllability. The method provides a scalable foundation for performance-aware, data-driven exploration in early-stage ship design.