Paper Citation Record · LEDGER

M4GN: Mesh-based Multi-segment Hierarchical Graph Network for Dynamic Simulations

As of 20 August 2026, this Paper Citation Record lists 29 of 29 outbound references and 1 inbound Pith citation observation for arXiv:2509.10659.

A citation records a reference. It does not transfer a finding from one paper to another.

pith.paper-citation-record.v1
2509.10659 v1

Coverage vector

measured 29 of 29 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-04T17:48:37.444438Z

measured 30 of 30 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-20T06:33:59.587034+00:00

measured 1 of 1 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-07-31T23:24:55.938007Z

measured 0 of 1 external citation measurements

A source-named dated measurement, never combined with another source.

Source: cited_works

Reference resolution

29 of 29 outbound references displayed

verified exact0
verified fuzzy0
unresolved29
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malformed identifier0
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External citation measurements

No source-named external measurement is stored.

Outbound references

Observation b92f9e35-9347-44e5-a75b-25c8af77d479 · outbound

This paper cites At this segmentation level, the model achieves the lowest RMSE and Chamfer Distance, indicating high prediction accuracy and precise shape representation.

M4GN: Mesh-based Multi-segment Hierarchical Graph Network for Dynamic Simulations At this segmentation level, the model achieves the lowest RMSE and Chamfer Distance, indicating high prediction accuracy and precise shape representation

Reference 7

Resolution: unresolved; no resolver link, observed 2026-08-04T17:48:37.109827Z

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Observation b574385c-0d27-47aa-8956-58bb7135b7c7 · outbound

This paper cites Semi-Supervised Classification with Graph Convolutional Networks.

M4GN: Mesh-based Multi-segment Hierarchical Graph Network for Dynamic Simulations Semi-Supervised Classification with Graph Convolutional Networks

Reference 11

Resolution: unresolved; no resolver link, observed 2026-08-04T17:48:35.062580Z

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Observation 3fef70c9-6e37-40ab-b564-538f65fa10b8 · outbound

This paper cites Towards Fast Simulation of Environmental Fluid Mechanics with Multi-Scale Graph Neural Networks.

M4GN: Mesh-based Multi-segment Hierarchical Graph Network for Dynamic Simulations Towards Fast Simulation of Environmental Fluid Mechanics with Multi-Scale Graph Neural Networks

Reference 13

Resolution: unresolved; no resolver link, observed 2026-08-04T17:48:35.326706Z

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Observation e02dc04e-9e51-49dc-85bc-563c96b084d4 · outbound

This paper cites Learning Mesh-Based Simulation with Graph Networks.

M4GN: Mesh-based Multi-segment Hierarchical Graph Network for Dynamic Simulations Learning Mesh-Based Simulation with Graph Networks

Reference 14

Resolution: unresolved; no resolver link, observed 2026-08-04T17:48:35.444207Z

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Observation 2f66556a-142c-4984-8fcd-413d4da1a0f0 · outbound

This paper cites U-net: Convolutional networks for biomedical image segmentation.

M4GN: Mesh-based Multi-segment Hierarchical Graph Network for Dynamic Simulations U-net: Convolutional networks for biomedical image segmentation

Reference 15

Resolution: unresolved; no resolver link, observed 2026-08-04T17:48:35.586655Z

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Observation e997e517-df68-4966-9e46-387dbd6522e2 · outbound

This paper cites Superpixels and supervoxels in an energy optimization framework.

M4GN: Mesh-based Multi-segment Hierarchical Graph Network for Dynamic Simulations Superpixels and supervoxels in an energy optimization framework

Reference 16

Resolution: unresolved; no resolver link, observed 2026-08-04T17:48:35.708332Z

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Observation 1df73cd0-1cd9-438d-ad12-cc2761c3ade5 · outbound

This paper cites Recent Advances on Machine Learning for Computational Fluid Dynamics: A Survey.

M4GN: Mesh-based Multi-segment Hierarchical Graph Network for Dynamic Simulations Recent Advances on Machine Learning for Computational Fluid Dynamics: A Survey

Reference 18

Resolution: unresolved; no resolver link, observed 2026-08-04T17:48:35.939705Z

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Observation 03c5dca1-2388-47cc-86e2-142250a8aac3 · outbound

This paper cites Revisiting Over-smoothing in Deep GCNs.

M4GN: Mesh-based Multi-segment Hierarchical Graph Network for Dynamic Simulations Revisiting Over-smoothing in Deep GCNs

Reference 19

Resolution: unresolved; no resolver link, observed 2026-08-04T17:48:36.103634Z

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Observation 6b0c9afc-dbb3-4360-961e-80689d001f81 · outbound

This paper cites For example, for triangular meshes, the aspect ratio is defined asLmax 2 √√ 3A , whereLmax is the longest edge length, A is the area of the triangle.

M4GN: Mesh-based Multi-segment Hierarchical Graph Network for Dynamic Simulations For example, for triangular meshes, the aspect ratio is defined asLmax 2 √√ 3A , whereLmax is the longest edge length, A is the area of the triangle

Reference 24

Resolution: unresolved; no resolver link, observed 2026-08-04T17:48:36.705084Z

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Observation c87e84b8-9f0c-497e-b4dd-147529d33344 · outbound

This paper cites Segment overlap (δ) δ = 0(none), δ = 1(one -ring) Helps Eulerian or directional meshes at highNseg (smoothertransitions); canadd redundancy and hurt Lagrangian cases.

M4GN: Mesh-based Multi-segment Hierarchical Graph Network for Dynamic Simulations Segment overlap (δ) δ = 0(none), δ = 1(one -ring) Helps Eulerian or directional meshes at highNseg (smoothertransitions); canadd redundancy and hurt Lagrangian cases

Reference 25

Resolution: unresolved; no resolver link, observed 2026-08-04T17:48:36.793004Z

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Observation 29a62fdb-6520-4b4d-82d6-0615051e0c48 · outbound

This paper cites an unresolved cited work.

M4GN: Mesh-based Multi-segment Hierarchical Graph Network for Dynamic Simulations Unresolved cited work

Reference 28

Resolution: unresolved; no resolver link, observed 2026-08-04T17:48:37.293855Z

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Observation 8bd85019-c2cd-4084-add2-a0802e11284c · outbound

This paper cites The time of our model tours is computed by adding the time used for segmentation and inference on a single NVIDIA Tesla P100 GPU.

M4GN: Mesh-based Multi-segment Hierarchical Graph Network for Dynamic Simulations The time of our model tours is computed by adding the time used for segmentation and inference on a single NVIDIA Tesla P100 GPU

Reference 29

Resolution: unresolved; no resolver link, observed 2026-08-04T17:48:37.444438Z

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Observation 6986691a-9c11-4d66-8385-15fe427abc4b · outbound

This paper cites an unresolved cited work.

M4GN: Mesh-based Multi-segment Hierarchical Graph Network for Dynamic Simulations Unresolved cited work

Reference 51

Resolution: unresolved; no resolver link, observed 2026-08-04T17:48:36.930622Z

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Observation e26214ff-af9e-4b6b-8e41-0d6bd6acd4c2 · outbound

This paper cites Node input includes mesh positionxi for CylinderFlow.

M4GN: Mesh-based Multi-segment Hierarchical Graph Network for Dynamic Simulations Node input includes mesh positionxi for CylinderFlow

Reference 128

Resolution: unresolved; no resolver link, observed 2026-08-04T17:48:36.478687Z

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Observation 08e77182-826e-4212-a115-5ec82d7e7a2a · outbound

This paper cites Eagle: Large-Scale Learning of Turbulent Fluid Dynamics with Mesh Transformers.

M4GN: Mesh-based Multi-segment Hierarchical Graph Network for Dynamic Simulations Eagle: Large-Scale Learning of Turbulent Fluid Dynamics with Mesh Transformers

Reference 1993

Resolution: unresolved; no resolver link, observed 2026-08-04T17:48:34.950332Z

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Observation 7b331ee8-3e9a-4b2d-be70-550bf30675e8 · outbound

This paper cites Propagation of ocean waves in discrete spectral wave models.Journal of Computational Physics, 68(2):307–326,.

M4GN: Mesh-based Multi-segment Hierarchical Graph Network for Dynamic Simulations Propagation of ocean waves in discrete spectral wave models.Journal of Computational Physics, 68(2):307–326,

Reference 1998

Resolution: unresolved; no resolver link, observed 2026-08-04T17:48:34.049835Z

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Observation de604f09-23bd-4a3d-bf12-2af0066150da · outbound

This paper cites 18 B Model Details 18 B.1 M4GN Configurations.

M4GN: Mesh-based Multi-segment Hierarchical Graph Network for Dynamic Simulations 18 B Model Details 18 B.1 M4GN Configurations

Reference 2005

Resolution: unresolved; no resolver link, observed 2026-08-04T17:48:36.337414Z

Source-reported events for the cited work

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Observation 4ab8f03c-8e21-4128-9aa9-3bf954727b98 · outbound

This paper cites Multiscale meshgraphnets.

M4GN: Mesh-based Multi-segment Hierarchical Graph Network for Dynamic Simulations Multiscale meshgraphnets

Reference 2007

Resolution: unresolved; no resolver link, observed 2026-08-04T17:48:34.493104Z

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Observation 065dc008-6018-4c07-b8d8-cbb4c7f6eb96 · outbound

This paper cites Learning Particle Dynamics for Manipulating Rigid Bodies, Deformable Objects, and Fluids.

M4GN: Mesh-based Multi-segment Hierarchical Graph Network for Dynamic Simulations Learning Particle Dynamics for Manipulating Rigid Bodies, Deformable Objects, and Fluids

Reference 2009

Resolution: unresolved; no resolver link, observed 2026-08-04T17:48:35.136017Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation a66dd874-cd70-4ffe-b3b8-6f212b351513 · outbound

This paper cites Order Matters: Sequence to sequence for sets.

M4GN: Mesh-based Multi-segment Hierarchical Graph Network for Dynamic Simulations Order Matters: Sequence to sequence for sets

Reference 2010

Resolution: unresolved; no resolver link, observed 2026-08-04T17:48:35.854280Z

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Unavailable: canonical work link unavailable.

Observation 4181d7b1-5054-4170-8053-0a908a44611f · outbound

This paper cites In our approach, we adapt SLIC to segment the mesh based on physics- informed features.

M4GN: Mesh-based Multi-segment Hierarchical Graph Network for Dynamic Simulations In our approach, we adapt SLIC to segment the mesh based on physics- informed features

Reference 2012

Resolution: unresolved; no resolver link, observed 2026-08-04T17:48:36.597973Z

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Observation fbcf5ed5-96aa-42c1-8b68-8d9d235e0419 · outbound

This paper cites Predicting Physics in Mesh-reduced Space with Temporal Attention.

M4GN: Mesh-based Multi-segment Hierarchical Graph Network for Dynamic Simulations Predicting Physics in Mesh-reduced Space with Temporal Attention

Reference 2013

Resolution: unresolved; no resolver link, observed 2026-08-04T17:48:34.902571Z

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Observation a6652a81-f167-451a-9752-1792ba81f6fb · outbound

This paper cites Empirical Evaluation of Gated Recurrent Neural Networks on Sequence Modeling.

M4GN: Mesh-based Multi-segment Hierarchical Graph Network for Dynamic Simulations Empirical Evaluation of Gated Recurrent Neural Networks on Sequence Modeling

Reference 2014

Resolution: unresolved; no resolver link, observed 2026-08-04T17:48:34.298032Z

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Observation c2855bef-b5ea-4b75-aebe-a51043d163d9 · outbound

This paper cites Graph Neural Networks with Learnable Structural and Positional Representations.

M4GN: Mesh-based Multi-segment Hierarchical Graph Network for Dynamic Simulations Graph Neural Networks with Learnable Structural and Positional Representations

Reference 2015

Resolution: unresolved; no resolver link, observed 2026-08-04T17:48:34.403907Z

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Observation 84c3610a-4990-4729-a9a5-338f2b980973 · outbound

This paper cites Learning Flexible Body Collision Dynamics with Hierarchical Contact Mesh Transformer.

M4GN: Mesh-based Multi-segment Hierarchical Graph Network for Dynamic Simulations Learning Flexible Body Collision Dynamics with Hierarchical Contact Mesh Transformer

Reference 2016

Resolution: unresolved; no resolver link, observed 2026-08-04T17:48:36.236797Z

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Observation 70febe48-7f76-406a-bbb1-f708be72209e · outbound

This paper cites Simple GNN Regularisation for 3D Molecular Property Prediction & Beyond.

M4GN: Mesh-based Multi-segment Hierarchical Graph Network for Dynamic Simulations Simple GNN Regularisation for 3D Molecular Property Prediction & Beyond

Reference 2019

Resolution: unresolved; no resolver link, observed 2026-08-04T17:48:34.760996Z

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Observation a856a8d9-1293-4620-bb45-e0094fea62e6 · outbound

This paper cites Learning Phrase Representations using RNN Encoder-Decoder for Statistical Machine Translation.

M4GN: Mesh-based Multi-segment Hierarchical Graph Network for Dynamic Simulations Learning Phrase Representations using RNN Encoder-Decoder for Statistical Machine Translation

Reference 2020

Resolution: unresolved; no resolver link, observed 2026-08-04T17:48:34.234465Z

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Observation c9b53a11-4390-4662-8f23-59d7ca24e968 · outbound

This paper cites Graph u-nets.

M4GN: Mesh-based Multi-segment Hierarchical Graph Network for Dynamic Simulations Graph u-nets

Reference 2022

Resolution: unresolved; no resolver link, observed 2026-08-04T17:48:34.554832Z

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Observation 3fc1467e-c516-478d-a574-511fe6a9fe98 · outbound

This paper cites A Compositional Object-Based Approach to Learning Physical Dynamics.

M4GN: Mesh-based Multi-segment Hierarchical Graph Network for Dynamic Simulations A Compositional Object-Based Approach to Learning Physical Dynamics

Reference 2023

Resolution: unresolved; no resolver link, observed 2026-08-04T17:48:34.146731Z

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Pith citing papers

Observation cb0fd642-5bdf-4ec9-ba89-1b597ee7e7f7 · inbound

Data-free neural PDE solvers based on Graph Neural Networks and weak forms cites this paper.

Data-free neural PDE solvers based on Graph Neural Networks and weak forms M4GN: Mesh-based Multi-segment Hierarchical Graph Network for Dynamic Simulations

Reference 15

Resolution: unresolved; no resolver link, observed 2026-07-31T23:24:55.938007Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.