SURF: A Generalization Benchmark for GNNs Predicting Fluid Dynamics
This addresses the need for reliable generalization in fluid dynamics simulations for design processes, but it is incremental as it focuses on benchmarking rather than introducing a new solver.
The authors tackled the problem of evaluating whether learned graph-based fluid simulators truly understand physics and generalize, rather than just interpolating, by proposing SURF, a benchmark with datasets and metrics for testing generalization across different topologies, resolutions, and thermodynamic ranges, and they demonstrated its applicability by investigating two state-of-the-art models, yielding new insights.
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.