Typed states for the displayed outbound observations.
Source: paper_references, paper_reference_links
Paper Citation Record · LEDGER
Using Machine Learning to Augment Coarse-Grid Computational Fluid Dynamics Simulations
As of 9 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 3 inbound Pith citation observations for arXiv:2010.00072.
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 2026-08-07T15:13:19.083703Z
A source-named dated measurement, never combined with another source.
Source: arxiv_reference, observed 2026-08-05T02:28:24.338817Z
Reference resolution
0 of 0 outbound references displayed
- verified exact0
- verified fuzzy0
- unresolved0
- parse uncertain0
- malformed identifier0
- metadata mismatch0
External citation measurements
20
arxiv_reference, observed 2026-08-05T02:28:24.338817Z
Outbound references
No outbound reference observations are available for this paper version.
Pith citing papers
Observation c749b474-74e4-4127-b684-4ea04bb8020e · inbound
Physics-based machine learning for mantle convection simulations cites this paper.
Physics-based machine learning for mantle convection simulations Using Machine Learning to Augment Coarse-Grid Computational Fluid Dynamics Simulations
Reference 36
- Resolution
- unresolved
- no resolver link, observed 2026-08-07T15:13:19.083703Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation beface70-b7e4-4aec-a7fa-7c9539df167c · inbound
sGPO: Trading Inference FLOPs for Training Efficiency in RLVR cites this paper.
sGPO: Trading Inference FLOPs for Training Efficiency in RLVR Using Machine Learning to Augment Coarse-Grid Computational Fluid Dynamics Simulations
Reference 138
- Resolution
- verified exact
- arxiv_id, observed 2026-07-02T23:17:29.268043Z
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 eb27fc12-0482-40ec-9be3-36c58502d4d4 · inbound
A Physics-Informed B-Spline Framework for Continuous Approximation of Flow Data cites this paper.
A Physics-Informed B-Spline Framework for Continuous Approximation of Flow Data Using Machine Learning to Augment Coarse-Grid Computational Fluid Dynamics Simulations
Reference 64
- Resolution
- verified exact
- arxiv_id, observed 2026-06-27T11:30:53.236038Z
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.