  In this work, we introduce ShipHullGAN, a generic parametric modeller built
using deep convolutional generative adversarial networks (GANs) for the
versatile representation and generation of ship hulls. At a high level, the new
model intends to address the current conservatism in the parametric ship design
paradigm, where parametric modellers can only handle a particular ship type. We
trained ShipHullGAN on a large dataset of 52,591 \textit{physically validated}
designs from a wide range of existing ship types, including container ships,
tankers, bulk carriers, tugboats, and crew supply vessels. We developed a new
shape extraction and representation strategy to convert all training designs
into a common geometric representation of the same resolution, as typically
GANs can only accept vectors of fixed dimension as input. A space-filling layer
is placed right after the generator component to ensure that the trained
generator can cover all design classes. During training, designs are provided
in the form of a shape-signature tensor (SST) which harnesses the compact
geometric representation using geometric moments that further enable the
inexpensive incorporation of physics-informed elements in ship design. We have
shown through extensive comparative studies and optimisation cases that
ShipHullGAN can generate designs with augmented features resulting in versatile
design spaces that produce traditional and novel designs with geometrically
valid and practically feasible shapes.
