SURF: A Generalization Benchmark for GNNs Predicting Fluid Dynamics

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 intere...

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Bibliographic Details
Published inarXiv.org
Main Authors Künzli, Stefan, Grötschla, Florian, Mathys, Joël, Wattenhofer, Roger
Format Paper
LanguageEnglish
Published Ithaca Cornell University Library, arXiv.org 20.11.2023
Subjects
Benchmarks
Fluid dynamics
Simulation
Simulators
Topology
Turbomachinery
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Summary: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.
ISSN:2331-8422