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Paper Citation Record · LEDGER

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

As of 8 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 3 inbound Pith citation observations for arXiv:2205.02637.

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

pith.paper-citation-record.v1
2205.02637 v1

Coverage vector

measured 0 of 0 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links

measured 3 of 3 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-08T06:32:00.761636+00:00

measured 3 of 3 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-06T18:44:49.316212Z

measured 0 of 1 external citation measurements

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

Source: pith, observed 2026-08-05T04:38:44.972183Z

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 7fce4633-f3ef-46ea-943e-09c46657f6cf · inbound

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

Recurrent U-Net-Based Graph Neural Network (RUGNN) for Accurate Deformation Predictions in Sheet Material Forming Towards Fast Simulation of Environmental Fluid Mechanics with Multi-Scale Graph Neural Networks

Reference 22

Resolution
unresolved
no resolver link, observed 2026-08-06T18:44:49.316212Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-06T18:44:49.316212Z digest=sha256:9b975a421ca0d555bb19b0aebf152bb8102dff874345b30be81770c37a59598d

Observation a80b28bf-5c50-488a-80cf-5746cf288bcc · inbound

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

Multi-Stage Graph Neural Networks for Data-Driven Prediction of Natural Convection in Enclosed Cavities Towards Fast Simulation of Environmental Fluid Mechanics with Multi-Scale Graph Neural Networks

Reference 41

Resolution
verified exact
local_arxiv, observed 2026-08-05T04:38:45.052442Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

source=pdf_text observed=2026-08-05T04:38:44.346836Z digest=sha256:12e1dc5c0c7698c302dd0478862157eacffb56a29fa307db02a98c4908e23ff1

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

M4GN: Mesh-based Multi-segment Hierarchical Graph Network for Dynamic Simulations cites this paper.

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

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-04T17:48:35.326706Z digest=sha256:85d013f18606ecbdd21c94fedaafbd207344552e86cb3d26d55fc22bc860f393