ShipNet: A Geometric Deep Learning Surrogate for Real-Time Ship Hydrodynamics
Accurate prediction of hydrodynamic performance is central to ship design, yet high-fidelity computational fluid dynamics remains prohibitively expensive for large-scale parametric exploration. This motivates the development of data-driven surrogate models that provide rapid approximations to hydrodynamic predictions at substantially reduced cost. We present ShipNet, a geometric deep-learning surrogate that predicts both hull-surface pressure distributions and far-field free-surface wave patterns directly from hull geometry and speed. The network employs a regularized dynamic graph convolutional backbone on hull point clouds, with a multi-head decoder for simultaneous near-body pressure a...
| Authors | Kirsten Odendaal, George Drakoulas |
|---|---|
| Primary category | physics.flu-dyn |
| Published | 13 June 2026 |
| Last updated at source | 13 June 2026 |
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