mlsanityData to 7 Oct 2026

Towards Fast Simulation of Environmental Fluid Mechanics with Multi-Scale Graph Neural Networks

arXiv 2205.02637 ↗First posted 5 May 2022physics.flu-dyn cs.LGcited by 11

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Abstract (arXiv)

Numerical simulators are essential tools in the study of natural fluid-systems, but their performance often limits application in practice. Recent machine-learning approaches have demonstrated their ability to accelerate spatio-temporal predictions, although, with only moderate accuracy in comparison. Here we introduce MultiScaleGNN, a novel multi-scale graph neural network model for learning to infer unsteady continuum mechanics in problems encompassing a range of length scales and complex boundary geometries. We demonstrate this method on advection problems and incompressible fluid dynamics, both fundamental phenomena in oceanic and atmospheric processes. Our results show good extrapolation to new domain geometries and parameters for long-term temporal simulations. Simulations obtained with MultiScaleGNN are between two and four orders of magnitude faster than those on which it was trained.

Key facts. Cited by 11 papers in the map (data through 7 Oct 2026). Builds on 24 of its 33 references that are in the map. First posted on arXiv 5 May 2022.

Builds on

24 of 33 references.

References · 24

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12 more
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Built on by

Cited by year (2026 to 7 Oct)

Citing papers per year: 2022: 1; 2023: 3; 2024: 1; 2025: 4; 2026: 2.

11 papers.

  1. 2022Efficient Learning of Mesh-Based Physical Simulation with BSMS-GNNcited by 43
  2. 2023Multi-Scale Simulation of Complex Systems: A Perspective of Integrating Knowledge and Datacited by 5
  3. 2023Multiscale Neural Operators for Solving Time-Independent PDEscited by 5
  4. 2023Multi-GPU Approach for Training of Graph ML Models on large CFD Meshescited by 4
  5. 2025A graph neural network surrogate model for mesh-based crashworthiness prediction of vehicle panel componentscited by 4
  6. 2024Beyond Grid Data: Exploring Graph Neural Networks for Earth Observationcited by 3
  7. 2026Adaptive Diffusion Posterior Sampling for Data and Model Fusion of Complex Nonlinear Dynamical Systemscited by 3
  8. 2025M4GN: Mesh-based Multi-segment Hierarchical Graph Network for Dynamic Simulationscited by 2
  9. 2025Recurrent U-Net-Based Graph Neural Network (RUGNN) for Accurate Deformation Predictions in Sheet Material Formingcited by 1
  10. 2025Multi-Stage Graph Neural Networks for Data-Driven Prediction of Natural Convection in Enclosed Cavitiescited by 1
  11. 2026Deterministic and probabilistic neural surrogates of global hybrid-Vlasov simulationscited by 0