Paper Citation Record · LEDGER

Harnessing Equivariance: Modeling Turbulence with Graph Neural Networks

As of 8 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 5 inbound Pith citation observations for arXiv:2504.07741.

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

pith.paper-citation-record.v1
2504.07741 v1

Coverage vector

measured 0 of 0 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links

measured 5 of 5 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 5 of 5 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-07T14:27:44.027178Z

measured 0 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-05-11T17:31:04.625936Z

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 7d53a405-fa89-4fbd-8500-9b1376eec1c6 · inbound

Hierarchical-embedding autoencoder with a predictor (HEAP) as efficient architecture for learning long-term evolution of complex multi-scale physical systems cites this paper.

Hierarchical-embedding autoencoder with a predictor (HEAP) as efficient architecture for learning long-term evolution of complex multi-scale physical systems Harnessing Equivariance: Modeling Turbulence with Graph Neural Networks

Reference 46

Resolution: unresolved; no resolver link, observed 2026-08-07T14:27:44.027178Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 202cf72a-9bf2-47f3-b8a7-eab15fa01772 · inbound

FIGNN: Feature-Specific Interpretability for Graph Neural Network Surrogate Models cites this paper.

FIGNN: Feature-Specific Interpretability for Graph Neural Network Surrogate Models Harnessing Equivariance: Modeling Turbulence with Graph Neural Networks

Reference 4

Resolution: unresolved; no resolver link, observed 2026-08-07T04:15:05.060035Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 55f97a0f-f520-41dd-919d-a496047858b4 · inbound

Reduced Subgrid Scale Terms in Three-Dimensional Turbulence cites this paper.

Reduced Subgrid Scale Terms in Three-Dimensional Turbulence Harnessing Equivariance: Modeling Turbulence with Graph Neural Networks

Reference 16

Resolution: unresolved; no resolver link, observed 2026-08-06T15:39:07.514347Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 8de34057-63df-4d4b-95f2-8fdce61177b7 · inbound

Turbulence teaches equivariance to neural networks cites this paper.

Turbulence teaches equivariance to neural networks Harnessing Equivariance: Modeling Turbulence with Graph Neural Networks

Reference 13

Resolution: unresolved; no resolver link, observed 2026-08-03T04:32:47.314334Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 5a2854fc-9234-4be3-ae34-ac2ddd22b791 · inbound

Deep Wave Network for Modeling Multi-Scale Physical Dynamics cites this paper.

Deep Wave Network for Modeling Multi-Scale Physical Dynamics Harnessing Equivariance: Modeling Turbulence with Graph Neural Networks

Reference 97

Resolution: verified exact; arxiv_id, observed 2026-05-11T17:31:04.639045Z

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.