Typed states for the displayed outbound observations.
Source: paper_references, paper_reference_links
Paper Citation Record · LEDGER
Physics-Informed Neural Networks: a Plug and Play Integration into Power System Dynamic Simulations
As of 15 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 2 inbound Pith citation observations for arXiv:2404.13325.
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 2026-08-15T06:32:42.880941+00:00
Pith citing papers itemized under the disclosed page cap.
Source: paper_references, paper_reference_links, observed 2026-08-08T15:36:54.906154Z
A source-named dated measurement, never combined with another source.
Source: arxiv_reference, observed 2026-05-16T02:47:10.854144Z
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 d9827894-e151-4fd1-af0b-1b2f6b5c2977 · 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 Neural Networks: a Plug and Play Integration into Power System Dynamic Simulations
Reference 2024
- Resolution
- unresolved
- no resolver link, observed 2026-08-08T15:36:54.906154Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation 05fcfc8b-0c9b-4be3-83aa-5353dbe82471 · inbound
Knowledge Integration in Differentiable Models: A Comparative Study of Data-Driven, Soft-Constrained, and Hard-Constrained Paradigms for Identification and Control of the Single Machine Infinite Bus System cites this paper.
Knowledge Integration in Differentiable Models: A Comparative Study of Data-Driven, Soft-Constrained, and Hard-Constrained Paradigms for Identification and Control of the Single Machine Infinite Bus System Physics-Informed Neural Networks: a Plug and Play Integration into Power System Dynamic Simulations
Reference 7
- Resolution
- verified exact
- arxiv_id, observed 2026-05-16T02:47:10.858020Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.