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English(EN) ShipNet: A Geometric Deep Learning Surrogate for Real-Time Ship Hydrodynamics

ShipNet AI模型实时预测船舶流体动力学

作者 PulseAugur 编辑部 · [1 个来源] ·

研究人员开发了ShipNet，一个用于实时预测船舶流体动力学的几何深度学习模型。该代理模型利用船体几何形状和速度来近似压力分布和波浪模式，与传统的计算流体动力学方法相比，速度显著提升。ShipNet在保留的测试集上取得了高精度，预测船体压力的R^2为0.98，预测波浪场的R^2为0.91，每次推理耗时约0.15秒。 AI

影响 通过提供快速、准确的流体动力学预测，加速船舶设计，从而能够进行更广泛的参数探索。

排序理由 该集群包含一篇详细介绍特定科学领域新AI模型的学术论文。[lever_c_demoted from research: ic=1 ai=1.0]

在 arXiv cs.LG 阅读 →

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ShipNet AI模型实时预测船舶流体动力学

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该集群包含一篇详细介绍特定科学领域新AI模型的学术论文。[lever_c_demoted from research: ic=1 ai=1.0]
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报道来源 [1]

  1. arXiv cs.LG TIER_1 English(EN) · Kirsten Odendaal, George Drakoulas ·

    ShipNet：实时船舶流体动力学的几何深度学习代理

    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 da…

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