Typed states for the displayed outbound observations.
Source: paper_references, paper_reference_links
Paper Citation Record · LEDGER
Learning Adaptive Hydrodynamic Models Using Neural ODEs in Complex Conditions
As of 9 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 4 inbound Pith citation observations for arXiv:2410.00490.
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 2026-08-09T06:31:02.800959+00:00
Pith citing papers itemized under the disclosed page cap.
Source: paper_references, paper_reference_links, observed 2026-08-02T23:34:49.848297Z
A source-named dated measurement, never combined with another source.
Source: arxiv_reference, observed 2026-07-03T00:47:30.235703Z
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 2252cd55-f662-4d88-89d9-f85591b7c8f4 · inbound
On the definition and importance of interpretability in scientific machine learning cites this paper.
On the definition and importance of interpretability in scientific machine learning Learning Adaptive Hydrodynamic Models Using Neural ODEs in Complex Conditions
Reference 76
- Resolution
- verified exact
- arxiv_id, observed 2026-05-22T14:11:38.636676Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.
Observation 465505d2-f8c0-4a5b-a675-19f4d4a28540 · inbound
Learning Gradient Flow: Using Equation Discovery to Accelerate Engineering Optimization cites this paper.
Learning Gradient Flow: Using Equation Discovery to Accelerate Engineering Optimization Learning Adaptive Hydrodynamic Models Using Neural ODEs in Complex Conditions
Reference 75
- Resolution
- unresolved
- no resolver link, observed 2026-08-02T23:34:49.848297Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation 31fbbf0c-fb17-4ee3-b952-6d7aa4f82a54 · inbound
From inverse problems to neural operators: prediction, mechanism, and generalization of data-driven models cites this paper.
From inverse problems to neural operators: prediction, mechanism, and generalization of data-driven models Learning Adaptive Hydrodynamic Models Using Neural ODEs in Complex Conditions
Reference 62
- Resolution
- verified exact
- arxiv_id, observed 2026-07-03T00:47:30.237934Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.
Observation 2fade0a1-a3d2-4261-a38e-2752e161ba54 · inbound
From inverse problems to neural operators: prediction, mechanism, and generalization of data-driven models cites this paper.
From inverse problems to neural operators: prediction, mechanism, and generalization of data-driven models Learning Adaptive Hydrodynamic Models Using Neural ODEs in Complex Conditions
Reference 62
- Resolution
- unresolved
- no resolver link, observed 2026-08-02T12:05:39.959287Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.