Typed states for the displayed outbound observations.
Source: paper_references, paper_reference_links
Paper Citation Record · LEDGER
Residual-based attention and connection to information bottleneck theory in PINNs
As of 22 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 5 inbound Pith citation observations for arXiv:2307.00379.
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 2026-08-22T06:32:14.747728+00:00
Pith citing papers itemized under the disclosed page cap.
Source: paper_references, paper_reference_links, observed 2026-08-11T00:55:34.939883Z
A source-named dated measurement, never combined with another source.
Source: arxiv_reference, observed 2026-07-02T00:16:23.959301Z
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 73be9df3-a89b-4c72-b867-0ef2d7e86e9f · inbound
Are Two Hidden Layers Still Enough for the Physics-Informed Neural Networks? cites this paper.
Are Two Hidden Layers Still Enough for the Physics-Informed Neural Networks? Residual-based attention and connection to information bottleneck theory in PINNs
Reference 60
- Resolution
- unresolved
- no resolver link, observed 2026-08-11T00:55:34.939883Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation 43cfc3b9-3d15-4589-88c0-03dd04954488 · inbound
On understanding and overcoming spectral biases of deep neural network learning methods for solving PDEs cites this paper.
On understanding and overcoming spectral biases of deep neural network learning methods for solving PDEs Residual-based attention and connection to information bottleneck theory in PINNs
Reference 135
- Resolution
- unresolved
- no resolver link, observed 2026-08-10T19:32:41.867699Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation d387455d-0e5c-44c2-a50e-9df9f9d790dd · inbound
A Practitioner's Guide to Kolmogorov-Arnold Networks cites this paper.
A Practitioner's Guide to Kolmogorov-Arnold Networks Residual-based attention and connection to information bottleneck theory in PINNs
Reference 163
- Resolution
- verified exact
- arxiv_id, observed 2026-05-18T03:30:50.848235Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.
Observation 6f195bab-1a0b-41b8-8002-b29a629613d6 · inbound
Physics informed operator learning of parameter dependent spectra cites this paper.
Physics informed operator learning of parameter dependent spectra Residual-based attention and connection to information bottleneck theory in PINNs
Reference 35
- Resolution
- verified exact
- arxiv_id, observed 2026-05-11T21:26:14.555214Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.
Observation f558972a-a5e2-423e-ac32-ad2c482c0599 · inbound
Neural Spectral Element Methods for stiff multiphysics PDEs with electrochemical transport benchmarks cites this paper.
Neural Spectral Element Methods for stiff multiphysics PDEs with electrochemical transport benchmarks Residual-based attention and connection to information bottleneck theory in PINNs
Reference 19
- Resolution
- verified exact
- arxiv_id, observed 2026-07-02T00:16:23.961423Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.