Simulating fluid dynamics is crucial for the design and development process, ranging from simple valves to complex turbomachinery. Accurately solving the underlying physical equations is computationally expensive. Therefore, learning-based solvers that model interactions on meshes have gained interest due to their promising speed-ups. However, it is unknown to what extent these models truly understand the underlying physical principles and can generalize rather than interpolate. Generalization is a key requirement for a general-purpose fluid simulator, which should adapt to different topologies, resolutions, or thermodynamic ranges. We propose SURF, a benchmark designed to test the \textit{generalization} of learned graph-based fluid simulators. SURF comprises individual datasets and provides specific performance and generalization metrics for evaluating and comparing different models. We empirically demonstrate the applicability of SURF by thoroughly investigating the two state-of-the-art graph-based models, yielding new insights into their generalization.

翻译：摘要：流体动力学模拟对从简单阀门到复杂涡轮机械的设计与开发过程至关重要。精确求解底层物理方程的计算成本高昂，因此基于学习的网格交互建模求解器因其显著的加速潜力而备受关注。然而，这些模型在多大程度上真正理解底层物理原理并具备泛化能力（而非内插能力）仍属未知。通用流体模拟器需要适应不同拓扑结构、分辨率或热力学范围，泛化性是其关键要求。我们提出SURF——一个专为测试基于图的流体模拟器泛化性而设计的基准。SURF包含独立数据集，并提供特定性能与泛化性指标用于评估和比较不同模型。通过对两种最先进的基于图的模型进行深入探究，我们实证证明了SURF的适用性，并获得了关于其泛化性的新见解。