Typed states for the displayed outbound observations.
Source: paper_references, paper_reference_links
Paper Citation Record · LEDGER
Unveiling the optimization process of Physics Informed Neural Networks: How accurate and competitive can PINNs be?
As of 7 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 3 inbound Pith citation observations for arXiv:2405.04230.
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 2026-08-03T18:51:35.478943Z
A source-named dated measurement, never combined with another source.
Source: arxiv_reference, observed 2026-05-17T01:58:51.570386Z
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 86465b31-704e-4049-965a-133b338aff1d · inbound
ATHENA: Agentic Team for Hierarchical Evolutionary Numerical Algorithms cites this paper.
ATHENA: Agentic Team for Hierarchical Evolutionary Numerical Algorithms Unveiling the optimization process of Physics Informed Neural Networks: How accurate and competitive can PINNs be?
Reference 65
- Resolution
- verified exact
- arxiv_id, observed 2026-05-17T01:58:51.572647Z
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.
Observation 34cd3c8c-d90a-4be2-ab60-5fadb720b663 · inbound
ATHENA: Agentic Team for Hierarchical Evolutionary Numerical Algorithms cites this paper.
ATHENA: Agentic Team for Hierarchical Evolutionary Numerical Algorithms Unveiling the optimization process of Physics Informed Neural Networks: How accurate and competitive can PINNs be?
Reference 65
- Resolution
- unresolved
- no resolver link, observed 2026-08-03T18:51:35.478943Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation 7b928174-7d52-4513-be23-f5843bcee538 · inbound
GRAFT-ATHENA: Self-Improving Agentic Teams for Autonomous Discovery and Evolutionary Numerical Algorithms cites this paper.
GRAFT-ATHENA: Self-Improving Agentic Teams for Autonomous Discovery and Evolutionary Numerical Algorithms Unveiling the optimization process of Physics Informed Neural Networks: How accurate and competitive can PINNs be?
Reference 36
- Resolution
- verified exact
- arxiv_id, observed 2026-05-13T07:07:27.256053Z
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.