arXiv Machine Learning June 16, 2026 By Kirsten Odendaal, George Drakoulas

ShipNet: A Geometric Deep Learning Surrogate for Real-Time Ship Hydrodynamics

Read the original on arXiv Machine Learning →

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.

Summary generated by The Flow from the publisher's feed. The full article lives at arXiv Machine Learning.

arXiv Machine Learning

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