arXiv:2606.15356v1 Announce Type: cross Abstract: 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 netwo
The proliferation of advanced deep learning techniques, especially in geometric deep learning, is enabling the creation of high-fidelity surrogate models for complex physical simulations.
This development significantly democratizes access to advanced hydrodynamic analysis, drastically reducing the cost and time associated with ship design and optimization.
Traditional computational fluid dynamics (CFD) for ship design will be augmented and potentially replaced by real-time, data-driven AI surrogates, accelerating development cycles.
- · Ship designers and manufacturers
- · Naval architecture firms
- · AI/ML model developers
- · Maritime logistics and shipping
- · Traditional CFD software vendors (if they don't adapt)
- · Firms reliant solely on expensive, slow simulation methods
Ship design and optimization processes become orders of magnitude faster and cheaper.
This could lead to a rapid increase in the iteration speed and innovation within the shipbuilding industry, including specialized vessel designs.
Reduced design costs and faster time-to-market could eventually impact global shipping costs and naval capabilities, potentially fostering more diverse and optimized fleets.
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Read at arXiv cs.LG