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 and free-surface elevation outputs. Training data consist of 420 inviscid free-surface simulations generated using a potential-flow panel method for two parent yacht hulls, each parameterized into 70 variants and evaluated at three speeds. ShipNet predicts per-point pressure coefficient and two-dimensional wave elevation map using a composite loss that combines point-wise regression and image-structure terms. On a geometry-held-out test set, ShipNet achieves R^2=0.98 for hull pressure and R^2=0.91 for wave fields. Inference requires approximately 0.15s per case, yielding over a 550x speedup relative to the potential-flow solver on conventional hardware. Limitations include the restricted geometry and speed ranges and the inviscid training data, while future work will extend the model to high-fidelity viscous simulations with physics-informed regularization.

翻译：船舶水动力性能的精确预测是船体设计的核心，然而高保真计算流体动力学在大规模参数化探索中仍因成本过高而受限。这促使了数据驱动代理模型的发展，该模型能以极低的成本快速逼近水动力预测结果。我们提出船网（ShipNet），一种几何深度学习代理模型，可直接从船体几何形状和航速预测船体表面压力分布及远场自由表面波模式。该网络在船体点云上采用正则化动态图卷积主干架构，并配备多头解码器以同步输出近体压力和自由表面高程。训练数据包含420个无粘自由表面模拟结果，这些结果采用势流面元法针对两种母型游艇船体生成，每种船体参数化为70个变体并在三个航速下进行评估。船网通过结合逐点回归与图像结构项的复合损失函数预测逐点压力系数和二维波高图。在几何保留测试集上，船网的船体压力R²=0.98、波场R²=0.91。每例推理约需0.15秒，相比常规硬件上的势流求解器实现超过550倍加速。当前局限包括受限的几何与航速范围以及无粘训练数据，未来工作将结合物理信息正则化将模型扩展至高保真粘性模拟。