Typed states for the displayed outbound observations.
Source: paper_references, paper_reference_links
Paper Citation Record · LEDGER
Convolutional Neural Operators for robust and accurate learning of PDEs
As of 4 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 2 inbound Pith citation observations for arXiv:2302.01178.
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 2026-08-03T06:30:56.289259+00:00
Pith citing papers itemized under the disclosed page cap.
Source: paper_references, paper_reference_links, observed 2026-05-24T09:36:59.102360Z
A source-named dated measurement, never combined with another source.
Source: arxiv_reference, observed 2026-05-24T09:39:17.190582Z
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 9a835247-d3f7-4583-9a6f-c167d237f3dc · inbound
Algorithmically Designed Artificial Neural Networks (ADANNs): Higher order deep operator learning for parametric partial differential equations cites this paper.
Algorithmically Designed Artificial Neural Networks (ADANNs): Higher order deep operator learning for parametric partial differential equations Convolutional Neural Operators for robust and accurate learning of PDEs
Reference 78
- Resolution
- verified exact
- arxiv_id, observed 2026-05-24T09:39:17.193970Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-03T06:30:56.289259+00:00.
Observation 3276e05e-db21-4cc7-b771-7d42f473ba4f · inbound
Spatiotemporal decoupled physics-informed Stone-Weierstrass neural operator for long-time prediction of time-dependent parametric PDEs cites this paper.
Spatiotemporal decoupled physics-informed Stone-Weierstrass neural operator for long-time prediction of time-dependent parametric PDEs Convolutional Neural Operators for robust and accurate learning of PDEs
Reference 18
- Resolution
- verified exact
- arxiv_id, observed 2026-05-19T18:57:43.153386Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-03T06:30:56.289259+00:00.