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：流体动力学仿真对于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需要高昂的计算成本。因此，基于学习的、在网格上建模交互的求解器因其显著的加速潜力而受到关注。然而，尚不清楚这些模型在多大程度上真正理解底层物理原理，并能够泛化而非仅进行插值。泛化能力是通用流体模拟器的核心要求，此类模拟器需适应不同的拓扑结构、分辨率或热力学范围。本文提出SURF——一个专为测试基于图的流体动力学学习模拟器$\textit{泛化能力}$而设计的基准。SURF包含独立数据集，并提供特定性能与泛化指标，用于评估和比较不同模型。我们通过对两种最先进的图模型进行深入研究，实证证明了SURF的适用性，并获得了关于其泛化能力的新见解。