Typed states for the displayed outbound observations.
Source: paper_references, paper_reference_links
Paper Citation Record · LEDGER
Dual Cone Gradient Descent for Training Physics-Informed Neural Networks
As of 8 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 2 inbound Pith citation observations for arXiv:2409.18426.
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 2026-08-07T06:34:17.273281+00:00
Pith citing papers itemized under the disclosed page cap.
Source: paper_references, paper_reference_links, observed 2026-08-06T20:11:32.854538Z
A source-named dated measurement, never combined with another source.
Source: pith, observed 2026-08-06T18:16:30.548575Z
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 d7667228-f466-4783-85c8-4095a0a83fdb · inbound
Taylor-Model Physics-Informed Neural Networks (PINNs) for Ordinary Differential Equations cites this paper.
Taylor-Model Physics-Informed Neural Networks (PINNs) for Ordinary Differential Equations Dual Cone Gradient Descent for Training Physics-Informed Neural Networks
Reference 13
- Resolution
- unresolved
- no resolver link, observed 2026-08-06T20:11:32.854538Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation bd4d5f85-f5f8-4bd1-b05c-d3ec7e796a33 · inbound
Breaking the Precision Ceiling in Physics-Informed Neural Networks: A Hybrid Fourier-Neural Architecture for Ultra-High Accuracy cites this paper.
Breaking the Precision Ceiling in Physics-Informed Neural Networks: A Hybrid Fourier-Neural Architecture for Ultra-High Accuracy Dual Cone Gradient Descent for Training Physics-Informed Neural Networks
Reference 21
- Resolution
- metadata mismatch
- local_arxiv, observed 2026-08-06T13:13:33.424912Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.