Abstract
When designing the hull form of a commercial ship, it is common practice to make manual adjustments to the shape of an existing reference hull form to meet design requirements. In efforts to generate a variety of hull forms, many shape optimization studies have either defined the hull form using parameters or directly modified the surface of the hull form, allowing for more flexible deformations. Recently, there has also been a surge of research utilizing deep learning-based generative models to generate new hull forms based on existing hull forms. While these methods offer more flexibility in shape deformation than traditional approaches, they have the limitation of needing to account for design requirements such as displacement and block coefficient of a ship.
