Typed states for the displayed outbound observations.
Source: paper_references, paper_reference_links
Paper Citation Record · LEDGER
Physics-Informed Graphical Neural Network for Parameter & State Estimations in Power Systems
As of 23 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 5 inbound Pith citation observations for arXiv:2102.06349.
A citation records a reference. It does not transfer a finding from one paper to another.
Coverage vector
One-hop event checks from named stored sources.
Source: scholarly_work_events, retraction_status_cache, observed 2026-08-23T06:30:58.430688+00:00
Pith citing papers itemized under the disclosed page cap.
Source: paper_references, paper_reference_links, observed 2026-08-08T15:36:54.896637Z
A source-named dated measurement, never combined with another source.
Source: arxiv_reference, observed 2026-05-25T04:35:21.076627Z
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 a70d4968-56d4-43e7-a21d-1d7f3355f107 · inbound
Physics-Informed Neural Networks for Accelerating Power System State Estimation cites this paper.
Physics-Informed Neural Networks for Accelerating Power System State Estimation Physics-Informed Graphical Neural Network for Parameter & State Estimations in Power Systems
Reference 8
- Resolution
- verified exact
- arxiv_id, observed 2026-05-24T05:56:01.703271Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.
Observation 487e6dbf-1724-4e16-b94c-5a42bd31314f · inbound
Toolbox for Developing Physics Informed Neural Networks for Power Systems Components cites this paper.
Toolbox for Developing Physics Informed Neural Networks for Power Systems Components Physics-Informed Graphical Neural Network for Parameter & State Estimations in Power Systems
Reference 9
- Resolution
- unresolved
- no resolver link, observed 2026-08-08T15:36:54.896637Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation 039fd48a-0cba-4850-bcf7-348418c0a23d · inbound
Robust Power System State Estimation using Physics-Informed Neural Networks cites this paper.
Robust Power System State Estimation using Physics-Informed Neural Networks Physics-Informed Graphical Neural Network for Parameter & State Estimations in Power Systems
Reference 27
- Resolution
- verified exact
- arxiv_id, observed 2026-05-19T06:22:07.919152Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.
Observation bd75b384-4aba-4ba7-8644-8c29283602cc · inbound
Learning Without Adversarial Training: A Physics-Informed Neural Network for Secure Power System State Estimation under False Data Injection Attacks cites this paper.
Learning Without Adversarial Training: A Physics-Informed Neural Network for Secure Power System State Estimation under False Data Injection Attacks Physics-Informed Graphical Neural Network for Parameter & State Estimations in Power Systems
Reference 18
- Resolution
- verified exact
- arxiv_id, observed 2026-05-13T19:43:11.343052Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.
Observation ded15468-49a1-4b3d-abac-f264c466345d · inbound
End-to-End Pseudo-Measurement Learning for State Estimation under Limited Observability cites this paper.
End-to-End Pseudo-Measurement Learning for State Estimation under Limited Observability Physics-Informed Graphical Neural Network for Parameter & State Estimations in Power Systems
Reference 12
- Resolution
- verified exact
- arxiv_id, observed 2026-05-25T04:35:21.079019Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.