Representation-aware generative modeling for early-stage ship design

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Htein, Nay Min and Louvros, Panagiotis and Stefanou, Evangelos and Hifi, Nabile and Boulougouris, Evangelos (2026) Representation-aware generative modeling for early-stage ship design. Journal of Marine Science and Engineering, 14 (7). 642. ISSN 2077-1312 (https://doi.org/10.3390/jmse14070642)

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Abstract

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.

ORCID iDs

Htein, Nay Min ORCID logoORCID: https://orcid.org/0009-0007-9534-4509, Louvros, Panagiotis ORCID logoORCID: https://orcid.org/0000-0001-8623-0680, Stefanou, Evangelos ORCID logoORCID: https://orcid.org/0009-0005-7586-4676, Hifi, Nabile and Boulougouris, Evangelos ORCID logoORCID: https://orcid.org/0000-0001-5730-007X;
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