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Paper Citation Record · LEDGER

X-MeshGraphNet: Scalable Multi-Scale Graph Neural Networks for Physics Simulation

As of 7 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 8 inbound Pith citation observations for arXiv:2411.17164.

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

pith.paper-citation-record.v1
2411.17164 v2

Coverage vector

measured 0 of 0 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links

measured 8 of 8 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-07T06:34:17.273281+00:00

measured 8 of 8 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-06T17:31:59.230034Z

measured 0 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-05-20T02:58:00.404945Z

Reference resolution

0 of 0 outbound references displayed

  • verified exact0
  • verified fuzzy0
  • unresolved0
  • parse uncertain0
  • malformed identifier0
  • metadata mismatch0

External citation measurements

No source-named external measurement is stored.

Outbound references

No outbound reference observations are available for this paper version.

Pith citing papers

Observation 5c7da1dc-e962-4a6a-999d-9ab050475979 · inbound

Rapid training of Hamiltonian graph networks using random features cites this paper.

Rapid training of Hamiltonian graph networks using random features X-MeshGraphNet: Scalable Multi-Scale Graph Neural Networks for Physics Simulation

Reference 58

Resolution
metadata mismatch
arxiv_id, observed 2026-05-19T10:17:15.658257Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

source=pdf_text observed=2026-05-19T10:17:00.343410Z digest=sha256:1da356f312ffb076984b318d3a6dc72d831dc6f3533f1f5d91864fd7db04371b

Observation 7ac881fa-b45f-40a8-bf97-621f7652d4e2 · inbound

A Benchmarking Framework for AI models in Automotive Aerodynamics cites this paper.

A Benchmarking Framework for AI models in Automotive Aerodynamics X-MeshGraphNet: Scalable Multi-Scale Graph Neural Networks for Physics Simulation

Reference 14

Resolution
unresolved
no resolver link, observed 2026-08-06T17:31:59.230034Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-06T17:31:59.230034Z digest=sha256:10f7f6d12336f4a011a891822f2a6dc1c9321feee287c1e0600d2003cf934955

Observation a02d3223-19b5-4033-98bb-58796d0811cc · inbound

Inferring processes within dynamic forest models using hybrid modeling cites this paper.

Inferring processes within dynamic forest models using hybrid modeling X-MeshGraphNet: Scalable Multi-Scale Graph Neural Networks for Physics Simulation

Reference 22

Resolution
unresolved
no resolver link, observed 2026-08-06T05:50:01.830578Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-06T05:50:01.830578Z digest=sha256:571a51786161d8f0f53036c945edef74bff80fae0d60d0d735ced92f0da5a8f1

Observation 982b6f5d-209e-4261-be61-71981ffbb1f0 · inbound

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

Point-wise Diffusion Models for Physical Systems with Shape Variations: Application to Spatio-temporal and Large-scale system X-MeshGraphNet: Scalable Multi-Scale Graph Neural Networks for Physics Simulation

Reference 25

Resolution
unresolved
no resolver link, observed 2026-08-06T05:45:56.393327Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-06T05:45:56.393327Z digest=sha256:c004ed4ee9e4f81f67eb405705f6e313e5bcbf9cb42e88a3de8e246900ab5743

Observation a1f6fbbb-a543-4033-9475-077299976887 · inbound

A Mixture of Experts Gating Network for Enhanced Surrogate Modeling in External Aerodynamics cites this paper.

A Mixture of Experts Gating Network for Enhanced Surrogate Modeling in External Aerodynamics X-MeshGraphNet: Scalable Multi-Scale Graph Neural Networks for Physics Simulation

Reference 16

Resolution
unresolved
no resolver link, observed 2026-08-05T14:29:30.596211Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-05T14:29:30.596211Z digest=sha256:5efd08f588e70b554a95282a055e8ff1f258b02dec324080e10d79796e0c9094

Observation 2bfec300-2d16-4307-b55a-8fcfabf2349c · inbound

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

GeoTransolver: Learning Physics on Irregular Domains Using Multi-scale Geometry Aware Physics Attention Transformer X-MeshGraphNet: Scalable Multi-Scale Graph Neural Networks for Physics Simulation

Reference 30

Resolution
unresolved
no resolver link, observed 2026-08-03T14:27:30.022548Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-03T14:27:30.022548Z digest=sha256:3801230d7e529f455079fe22819936340fad9416a30274349ceb3c6d61371e8d

Observation 3de307da-81c1-402c-af6d-fa2c8d447f5b · inbound

HiLiftAeroML: High-Fidelity Computational Fluid Dynamics Dataset for High-Lift Aircraft Aerodynamics cites this paper.

HiLiftAeroML: High-Fidelity Computational Fluid Dynamics Dataset for High-Lift Aircraft Aerodynamics X-MeshGraphNet: Scalable Multi-Scale Graph Neural Networks for Physics Simulation

Reference 17

Resolution
verified exact
arxiv_id, observed 2026-05-20T02:58:00.408456Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

source=arxiv_source observed=2026-05-20T02:53:28.867588Z digest=sha256:4fdd2d89d90865a540c917c94450c84d3b0892fc9ba597c2dad2e70134b5f40d

Observation 9f1f2260-9a04-448f-907a-92b46d4e56d6 · inbound

CoFINN: Conservation Flux Informed Neural Networks for Physics Problems Governed by Conservation Laws cites this paper.

CoFINN: Conservation Flux Informed Neural Networks for Physics Problems Governed by Conservation Laws X-MeshGraphNet: Scalable Multi-Scale Graph Neural Networks for Physics Simulation

Reference 34

Resolution
unresolved
no resolver link, observed 2026-07-11T19:51:56.544026Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-07-11T19:51:56.544026Z digest=sha256:b86f90b05f0ae996812c9bdd0c08de5ed6531c65d0f8ead60d4f9c68db5176e9