Typed states for the displayed outbound observations.
Source: paper_references, paper_reference_links
Paper Citation Record · LEDGER
Towards Physics-informed Deep Learning for Turbulent Flow Prediction
As of 17 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 3 inbound Pith citation observations for arXiv:1911.08655.
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 2026-08-17T06:30:58.91139+00:00
Pith citing papers itemized under the disclosed page cap.
Source: paper_references, paper_reference_links, observed 2026-08-11T13:31:12.101726Z
A source-named dated measurement, never combined with another source.
Source: arxiv_reference, observed 2026-07-02T23:17:29.263813Z
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 324a4b46-6a3e-4eab-8c6b-b4ea62078e19 · inbound
Predicting Change, Not States: An Alternate Framework for Neural PDE Surrogates cites this paper.
Predicting Change, Not States: An Alternate Framework for Neural PDE Surrogates Towards Physics-informed Deep Learning for Turbulent Flow Prediction
Reference 41
- Resolution
- unresolved
- no resolver link, observed 2026-08-11T13:31:12.101726Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation 67395cf1-81ac-4066-a128-d447280d0a7f · inbound
Dual-Correction Physics-Informed Neural Networks for Hemodynamic Reconstruction from Sparse Data cites this paper.
Dual-Correction Physics-Informed Neural Networks for Hemodynamic Reconstruction from Sparse Data Towards Physics-informed Deep Learning for Turbulent Flow Prediction
Reference 9
- Resolution
- verified exact
- arxiv_id, observed 2026-05-14T21:59:30.043425Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.
Observation 5fd4c84f-66fe-4a1b-8c1e-b2af3530fbf0 · inbound
sGPO: Trading Inference FLOPs for Training Efficiency in RLVR cites this paper.
sGPO: Trading Inference FLOPs for Training Efficiency in RLVR Towards Physics-informed Deep Learning for Turbulent Flow Prediction
Reference 135
- Resolution
- metadata mismatch
- arxiv_id, observed 2026-07-02T23:17:29.265322Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.