Ship Hull Generation Using GANs & VAE-PCTs: A Comparison Study

This paper introduces and employs a Computer-Aided Design CAD-to-CAD policy in the context of developing generative models for Machine Learning (ML)-supported hull-form exploration of design spaces in the context of early phases of the ship-design process, using CAD databases as training datasets. For this purpose, two generative models are constructed, a convolutional Generative Adversarial Network (GAN) and a Variational Autoencoder with a Point Cloud Transformer (VAE-PCT) model, that utilize two distinct training datasets of single-type hulls, one consisting of simple (bulbous-bow-free) ship hulls and another consisting of hulls featuring a typical containership. Both models adopt the same CAD-hull discretization, whose combinatorial structure is equivalent to that of a 3D rectangular matrix, and a normalization that proves beneficial regarding the stability, convergence, and time cost of the training process. The architecture of the VAE-PCT model is characterized by a VAE backbone enriched with stacked Point Transformer blocks in the decoder component. The models are compared in terms of both quality and diversity, using five complementary metrics commonly used in the elevant literature. Finally, the CAD-to-CAD policy is materialized by constructing a pull-back map from the generator’s discrete output to a B-spline surface obtained by lofting a family of 3D curves stemming from fairing the generator’s output. The performance of the pull-back map is tested against the geometric validity of the obtained ship hulls and the statistics of (i) nine non-dimensional geometric coefficients, which are correlated with critical performance and operational Key Performance Indicators (KPIs) for ship design, and (ii) calm-water resistance, using an estimator appropriate for the early ship-design phase. The comparison, involving the training CAD models and 8000 CAD hulls obtained by processing the discrete output of the GAN and VAE-PCT generators, confirm that the statistics of the generated CAD hulls are well aligned with those of the training ones.