Typed states for the displayed outbound observations.
Source: paper_references, paper_reference_links
Paper Citation Record · LEDGER
Data-driven Koopman operator predictions of turbulent dynamics in models of shear flows
As of 14 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 2 inbound Pith citation observations for arXiv:2407.16542.
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 2026-08-12T17:08:12.761963Z
A source-named dated measurement, never combined with another source.
Source: arxiv_reference, observed 2026-05-11T12:26:11.325807Z
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 238f5128-35ac-4bb7-84f5-ddea637851e5 · 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 Data-driven Koopman operator predictions of turbulent dynamics in models of shear flows
Reference 46
- Resolution
- unresolved
- no resolver link, observed 2026-08-12T17:08:12.761963Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation 9b5859f7-4803-490b-a9ae-2ecb14eb4fee · inbound
Autoregressive prediction of 2D MHD dynamics inferred from deep learning modeling cites this paper.
Autoregressive prediction of 2D MHD dynamics inferred from deep learning modeling Data-driven Koopman operator predictions of turbulent dynamics in models of shear flows
Reference 26
- Resolution
- verified exact
- arxiv_id, observed 2026-05-11T12:26:11.328721Z
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.