Typed states for the displayed outbound observations.
Source: paper_references, paper_reference_links
Paper Citation Record · LEDGER
Flow reconstruction in time-varying geometries using graph neural networks
As of 8 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 2 inbound Pith citation observations for arXiv:2411.08764.
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 2026-08-08T06:32:00.761636+00:00
Pith citing papers itemized under the disclosed page cap.
Source: paper_references, paper_reference_links, observed 2026-08-07T13:40:19.564763Z
A source-named dated measurement, never combined with another source.
Source: arxiv_reference, observed 2026-06-29T10:13:17.893398Z
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 7172c0e7-5521-4221-a7f9-cea5e90b865c · inbound
A Physics-Augmented GraphGPS Framework for the Reconstruction of 3D Riemann Problems from Sparse Data cites this paper.
A Physics-Augmented GraphGPS Framework for the Reconstruction of 3D Riemann Problems from Sparse Data Flow reconstruction in time-varying geometries using graph neural networks
Reference 32
- Resolution
- unresolved
- no resolver link, observed 2026-08-07T13:40:19.564763Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation e24aa90d-e28b-4e6d-aa6b-218f75002d5b · inbound
Data-efficient semi-supervised learning for flow estimation using unlabelled probe data cites this paper.
Data-efficient semi-supervised learning for flow estimation using unlabelled probe data Flow reconstruction in time-varying geometries using graph neural networks
Reference 10
- Resolution
- verified exact
- arxiv_id, observed 2026-06-29T10:13:17.894661Z
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.