Autonomous ground vehicles operating in shallow water or flood-prone terrains require dynamic models that account for hydrodynamic forces. However, the simulation and planning tools currently available either lack the physical fidelity or are too computationally expensive to run in real time. This work presents a per-surface neural network surrogate that bridges this gap by predicting geometry-resolved hydrodynamic forces at real-time rates, trained entirely on high-fidelity CFD data from two geometrically distinct vehicles. A vehicle specific Signed Distance Field (SDF) provides per-surface submergence inputs, allowing the model to resolve how loading varies with vehicle geometry, depth, and flow direction. On held-out CFD data, the surrogate achieves a longitudinal-force symmetric MAPE (sMAPE) of 13\% and a vertical-force sMAPE of 3-12\%, with inference running under 0.9\,ms per sample. To evaluate the model under real-world conditions, water wading trials of a full-scale vehicle at different submersion depths are used. Motion capture derived kinematics serve as the surrogate inputs, and the resulting predictions are tested to reproduce known physical relationships between force, speed, and depth. The predicted drag follows quadratic speed scaling ($R^2 \geq 0.97$) and the buoyancy intercepts scale linearly with depth ($R^2 = 0.973$). Neither relationship is encoded in the model training loss, both emerge from the per-surface architecture summing individually predicted surface forces. The resulting framework provides a pathway for embedding physically grounded hydrodynamics into the simulation and planning loops that autonomous ground vehicles depend on in amphibious environments.

翻译：运行于浅水或易涝地形中的自主地面车辆需要能考虑水动力的动态模型。然而，目前可用的仿真与规划工具要么缺乏物理保真度，要么计算成本过高而无法实时运行。本文提出了一种每表面神经网络代理模型，通过以实时速率预测几何解析的水动力来弥合这一差距，该模型完全基于来自两种几何形状不同车辆的高保真CFD数据训练。车辆特有的符号距离场（SDF）提供每表面浸没深度输入，使模型能够解析载荷随车辆几何、深度和流向的变化。在保留的CFD数据上，该代理模型实现了纵向力对称平均绝对百分比误差（sMAPE）为13%，垂直力sMAPE为3-12%，每个样本的推理时间低于0.9毫秒。为评估模型在真实条件下的表现，采用了全尺寸车辆在不同浸没深度下的涉水试验数据。基于运动捕捉获得的速度作为代理模型输入，并测试预测结果以复现力、速度与深度之间已知的物理关系。预测阻力遵循速度二次方关系（$R^2 \geq 0.97$），浮力截距随深度线性变化（$R^2 = 0.973$）。这两种关系均未编码在模型训练损失中，而是通过每表面架构累加各自预测的表面力自然涌现。该框架为将基于物理的水动力嵌入两栖环境中自主地面车辆依赖的仿真与规划循环提供了可行路径。