Pith. sign in

REVIEW 11 cited by

MultiScale MeshGraphNets

Not yet reviewed by Pith; the record is open.

Re-run · record.json Download PDF Read on arXiv ↗

This paper has not been read by Pith yet. Machine review is queued; the pith claim, tier, and objections will appear here once it completes.

SPECIMEN: schema-true, not a live event

T0 review · schema-true

One-sentence machine reading of the paper's core claim.

pith:XXXXXXXX · record.json · timestamp

arxiv 2210.00612 v1 pith:ON2PA7K5 submitted 2022-10-02 cs.LG cs.CE

Meire Fortunato , Tobias Pfaff , Peter Wirnsberger , Alexander Pritzel , Peter Battaglia This is my paper
classification cs.LGcs.CE
keywords meshgraphnetshigh-resolutionaccuracyaccurategraphimprovingmessagemultiscale
verification ladder T0 review T1 audit T2 compute T3 formal
0 comments
read the original abstract

In recent years, there has been a growing interest in using machine learning to overcome the high cost of numerical simulation, with some learned models achieving impressive speed-ups over classical solvers whilst maintaining accuracy. However, these methods are usually tested at low-resolution settings, and it remains to be seen whether they can scale to the costly high-resolution simulations that we ultimately want to tackle. In this work, we propose two complementary approaches to improve the framework from MeshGraphNets, which demonstrated accurate predictions in a broad range of physical systems. MeshGraphNets relies on a message passing graph neural network to propagate information, and this structure becomes a limiting factor for high-resolution simulations, as equally distant points in space become further apart in graph space. First, we demonstrate that it is possible to learn accurate surrogate dynamics of a high-resolution system on a much coarser mesh, both removing the message passing bottleneck and improving performance; and second, we introduce a hierarchical approach (MultiScale MeshGraphNets) which passes messages on two different resolutions (fine and coarse), significantly improving the accuracy of MeshGraphNets while requiring less computational resources.

Discussion (0). Sign in to comment.

Forward citations

Cited by 11 Pith papers

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. OpenAlex reports about 26 citations worldwide. Full citation record

  1. Accurate Mediterranean Sea forecasting via graph-based deep learning

    physics.ao-ph 2025-06 conditional novelty 7.0 of 10

    SeaCast, a graph neural network, makes 15-day Mediterranean Sea forecasts that outperform the operational MedFS system over the evaluated period, while producing a forecast in 20 seconds on one GPU.

  2. Scale-Aware Learning of Chaotic Dynamics on Unstructured Meshes via Binned Spectral Losses

    cs.LG 2026-07 conditional novelty 6.0 of 10

    Adding graph-Laplacian band-power matching to the training loss improves long-horizon autoregressive forecasting of chaotic flows on unstructured meshes.

  3. GenDA: Generative Data Assimilation on Complex Urban Areas via Classifier-Free Diffusion Guidance

    cs.LG 2026-01 conditional novelty 6.0 of 10

    A sensor-conditioned graph diffusion model reconstructs urban wind fields from <1% of mesh nodes, beating supervised GNN and SVD baselines on held-out altitude slices of one RANS dataset.

  4. Point-wise Diffusion Models for Physical Systems with Shape Variations: Application to Spatio-temporal and Large-scale system

    physics.comp-ph 2025-08 unverdicted novelty 6.0 of 10

    A point-wise diffusion transformer predicts spatio-temporal physical fields on arbitrary meshes and point clouds, claiming up to 200x faster inference and better accuracy than image-based diffusion surrogates.

  5. HEIST: A Graph Foundation Model for Spatial Transcriptomics and Proteomics Data

    q-bio.GN 2025-06 conditional novelty 6.0 of 10

    HEIST pretrains a hierarchical graph transformer on 22.3 million cells so that one frozen encoder can annotate cell types, cluster cells, impute missing genes, and predict clinical outcomes in spatial transcriptomics ...

  6. A Mixture of Experts Gating Network for Enhanced Surrogate Modeling in External Aerodynamics

    cs.LG 2025-08 conditional novelty 5.0 of 10

    A mixture-of-experts gating network that fuses predictions from DoMINO, X-MeshGraphNet, and FigConvNet reduces L-2 prediction error for automotive surface pressure and wall shear stress below each individual expert on...

  7. Inferring processes within dynamic forest models using hybrid modeling

    q-bio.QM 2025-08 unverdicted novelty 5.0 of 10

    The abstract claims a hybrid gap-model plus neural-network approach, FINN, improves forest growth inference and forecasting, but the manuscript body is an unrelated diffusion-model paper, so the abstract's claims are ...

  8. Recurrent U-Net-Based Graph Neural Network (RUGNN) for Accurate Deformation Predictions in Sheet Material Forming

    cs.LG 2025-07 conditional novelty 5.0 of 10

    RUGNN, a recurrent U-Net graph neural network with a node-to-surface contact feature, predicts sheet metal deformation fields across stamping timesteps with lower accumulated error than three GNN baselines on two FE-b...

  9. FIGNN: Feature-Specific Interpretability for Graph Neural Network Surrogate Models

    cs.LG 2025-06 conditional novelty 5.0 of 10

    FIGNN adds per-feature Top-K masking branches to a frozen GNN surrogate, producing variable-specific spatial attributions and error budgets for climate and fluid dynamics forecasts.

  10. GeoTransolver: Learning Physics on Irregular Domains Using Multi-scale Geometry Aware Physics Attention Transformer

    cs.LG 2025-12 conditional novelty 4.0 of 10

    GeoTransolver, a geometry-aware attention transformer, improves surrogate CFD accuracy over existing baselines on three automotive/aerospace datasets, but the paper has major reporting gaps.

  11. Multi-Stage Graph Neural Networks for Data-Driven Prediction of Natural Convection in Enclosed Cavities

    cs.CV 2025-09 reject novelty 4.0 of 10

    A multi-stage GNN with hierarchical pooling and unpooling predicts natural-convection temperature fields more accurately and efficiently than a single-scale MeshGraphNets baseline on a new 2D cavity dataset.

Pith tools