Typed states for the displayed outbound observations.
Source: paper_references, paper_reference_links
Paper Citation Record · LEDGER
TENG: Time-Evolving Natural Gradient for Solving PDEs With Deep Neural Nets Toward Machine Precision
As of 8 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 3 inbound Pith citation observations for arXiv:2404.10771.
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 2026-08-08T06:32:00.761636+00:00
Pith citing papers itemized under the disclosed page cap.
Source: paper_references, paper_reference_links, observed 2026-08-06T22:01:48.763725Z
A source-named dated measurement, never combined with another source.
Source: arxiv_reference, observed 2026-05-10T09:23:37.349673Z
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 0d61447b-51fb-4aa5-bdf4-50cd1f900179 · inbound
BWLer: Barycentric Weight Layer Elucidates a Precision-Conditioning Tradeoff for PINNs cites this paper.
BWLer: Barycentric Weight Layer Elucidates a Precision-Conditioning Tradeoff for PINNs TENG: Time-Evolving Natural Gradient for Solving PDEs With Deep Neural Nets Toward Machine Precision
Reference 10
- Resolution
- unresolved
- no resolver link, observed 2026-08-06T22:01:48.763725Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation 9d840951-95f7-4966-8c99-fff55d42c90f · inbound
Error analysis for learning the time-stepping operator of evolutionary PDEs cites this paper.
Error analysis for learning the time-stepping operator of evolutionary PDEs TENG: Time-Evolving Natural Gradient for Solving PDEs With Deep Neural Nets Toward Machine Precision
Reference 22
- Resolution
- unresolved
- no resolver link, observed 2026-08-05T10:24:35.673150Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation 93a4e9cb-d1dd-4880-b019-c9dd85dbbdc7 · inbound
Singularity Formation: Synergy in Theoretical, Numerical and Machine Learning Approaches cites this paper.
Singularity Formation: Synergy in Theoretical, Numerical and Machine Learning Approaches TENG: Time-Evolving Natural Gradient for Solving PDEs With Deep Neural Nets Toward Machine Precision
Reference 64
- Resolution
- verified exact
- arxiv_id, observed 2026-05-10T09:23:37.351406Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.