Typed states for the displayed outbound observations.
Source: paper_references, paper_reference_links
Paper Citation Record · LEDGER
Leveraging KANs For Enhanced Deep Koopman Operator Discovery
As of 14 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 2 inbound Pith citation observations for arXiv:2406.02875.
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 2026-08-14T06:32:32.682623+00:00
Pith citing papers itemized under the disclosed page cap.
Source: paper_references, paper_reference_links, observed 2026-08-12T17:08:12.648471Z
A source-named dated measurement, never combined with another source.
Source: arxiv_reference, observed 2026-05-10T09:23:37.374723Z
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 0e75164c-2ad8-405e-aae7-20faabfeb986 · inbound
On the relationship between Koopman operator approximations and neural ordinary differential equations for data-driven time-evolution predictions cites this paper.
On the relationship between Koopman operator approximations and neural ordinary differential equations for data-driven time-evolution predictions Leveraging KANs For Enhanced Deep Koopman Operator Discovery
Reference 23
- Resolution
- unresolved
- no resolver link, observed 2026-08-12T17:08:12.648471Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation e67083ac-0f40-46f3-8b92-12bfa4cdfa39 · inbound
Singularity Formation: Synergy in Theoretical, Numerical and Machine Learning Approaches cites this paper.
Singularity Formation: Synergy in Theoretical, Numerical and Machine Learning Approaches Leveraging KANs For Enhanced Deep Koopman Operator Discovery
Reference 271
- Resolution
- verified exact
- arxiv_id, observed 2026-05-10T09:23:37.376405Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.