Typed states for the displayed outbound observations.
Source: paper_references, paper_reference_links
Paper Citation Record · LEDGER
Graph Neural Networks for Aerodynamic Flow Reconstruction from Sparse Sensing
As of 19 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 3 inbound Pith citation observations for arXiv:2301.03228.
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 2026-08-19T06:32:44.657259+00:00
Pith citing papers itemized under the disclosed page cap.
Source: paper_references, paper_reference_links, observed 2026-08-12T21:26:22.718699Z
A source-named dated measurement, never combined with another source.
Source: pith, observed 2026-08-07T13:40:21.690285Z
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 3186003b-d8d9-45b3-a05d-e504eb27aeff · inbound
Flow reconstruction in time-varying geometries using graph neural networks cites this paper.
Flow reconstruction in time-varying geometries using graph neural networks Graph Neural Networks for Aerodynamic Flow Reconstruction from Sparse Sensing
Reference 17
- Resolution
- unresolved
- no resolver link, observed 2026-08-12T21:26:22.718699Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation 81c0eaa5-e495-4b98-9c41-a9f12cd28750 · inbound
Graph Transformers for inverse physics: reconstructing flows around arbitrary 2D airfoils cites this paper.
Graph Transformers for inverse physics: reconstructing flows around arbitrary 2D airfoils Graph Neural Networks for Aerodynamic Flow Reconstruction from Sparse Sensing
Reference 17
- Resolution
- unresolved
- no resolver link, observed 2026-08-10T04:53:36.002210Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation 2667d021-75fd-467c-bb9c-16496437fddc · 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 Graph Neural Networks for Aerodynamic Flow Reconstruction from Sparse Sensing
Reference 27
- Resolution
- verified exact
- local_arxiv, observed 2026-08-07T13:40:21.765273Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.