mlsanityData to 7 Oct 2026

An extensible Benchmarking Graph-Mesh dataset for studying Steady-State Incompressible Navier-Stokes Equations

arXiv 2206.14709 ↗First posted 29 Jun 2022cs.LG cs.CV cs.NAcited by 5

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Abstract (arXiv)

Recent progress in Geometric Deep Learning (GDL) has shown its potential to provide powerful data-driven models. This gives momentum to explore new methods for learning physical systems governed by Partial Differential Equations (PDEs) from Graph-Mesh data. However, despite the efforts and recent achievements, several research directions remain unexplored and progress is still far from satisfying the physical requirements of real-world phenomena. One of the major impediments is the absence of benchmarking datasets and common physics evaluation protocols. In this paper, we propose a 2-D graph-mesh dataset to study the airflow over airfoils at high Reynolds regime (from 106 and beyond). We also introduce metrics on the stress forces over the airfoil in order to evaluate GDL models on important physical quantities. Moreover, we provide extensive GDL baselines.

Key facts. Cited by 5 papers in the map (data through 7 Oct 2026). Builds on 31 of its 48 references that are in the map. First posted on arXiv 29 Jun 2022.

Builds on

31 of 48 references.

References · 31

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Cited by year

Citing papers per year: 2022: 1; 2023: 1; 2024: 3.

5 papers.

  1. 2022AirfRANS: High Fidelity Computational Fluid Dynamics Dataset for Approximating Reynolds-Averaged Navier-Stokes Solutionscited by 77
  2. 2023Multiscale Graph Neural Network Autoencoders for Interpretable Scientific Machine Learningcited by 18
  3. 2024FlowBench: A Large Scale Benchmark for Flow Simulation over Complex Geometriescited by 18
  4. 2024Generalization capabilities of MeshGraphNets to unseen geometries for fluid dynamicscited by 3
  5. 2024Sampling-based Distributed Training with Message Passing Neural Networkcited by 2