Typed states for the displayed outbound observations.
Source: paper_references, paper_reference_links
Paper Citation Record · LEDGER
Stacked networks improve physics-informed training: applications to neural networks and deep operator networks
As of 8 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 3 inbound Pith citation observations for arXiv:2311.06483.
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 2026-08-07T13:17:08.089650Z
A source-named dated measurement, never combined with another source.
Source: arxiv_reference, observed 2026-05-18T03:30:50.672220Z
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 ea5853a1-f9c1-4af8-995f-e7a4a0a9c6b1 · inbound
Multiprecision computing for multistage fractional physics-informed neural networks cites this paper.
Multiprecision computing for multistage fractional physics-informed neural networks Stacked networks improve physics-informed training: applications to neural networks and deep operator networks
Reference 3
- Resolution
- unresolved
- no resolver link, observed 2026-08-07T13:17:08.089650Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation 2ee1fcc5-a004-4a0a-abac-eae67f9c403a · inbound
A Practitioner's Guide to Kolmogorov-Arnold Networks cites this paper.
A Practitioner's Guide to Kolmogorov-Arnold Networks Stacked networks improve physics-informed training: applications to neural networks and deep operator networks
Reference 164
- Resolution
- verified exact
- arxiv_id, observed 2026-05-18T03:30:50.675262Z
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.
Observation ec303573-8204-41bd-948b-a7a1a0c44071 · inbound
High-Precision Phase-Shift Transferable Neural Networks for High-Frequency Function Approximation and PDE Solution cites this paper.
High-Precision Phase-Shift Transferable Neural Networks for High-Frequency Function Approximation and PDE Solution Stacked networks improve physics-informed training: applications to neural networks and deep operator networks
Reference 20
- Resolution
- verified exact
- arxiv_id, observed 2026-05-13T18:38:07.533014Z
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.