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Paper Citation Record · LEDGER

Solving the Regge-Wheeler and Teukolsky equations: supervised versus unsupervised physics-informed neural networks

As of 18 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 5 inbound Pith citation observations for arXiv:2402.11343.

A citation records a reference. It does not transfer a finding from one paper to another.

pith.paper-citation-record.v1
2402.11343 v3

Coverage vector

measured 0 of 0 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links

measured 5 of 5 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-18T06:34:40.430872+00:00

measured 5 of 5 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-06T15:30:31.948943Z

measured 0 of 1 external citation measurements

A source-named dated measurement, never combined with another source.

Source: arxiv_reference, observed 2026-07-04T08:19:44.089082Z

Reference resolution

0 of 0 outbound references displayed

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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 badbecaf-5257-425e-865c-1bfe858e6ecc · inbound

Learning Null Geodesics for Gravitational Lensing Rendering in General Relativity cites this paper.

Learning Null Geodesics for Gravitational Lensing Rendering in General Relativity Solving the Regge-Wheeler and Teukolsky equations: supervised versus unsupervised physics-informed neural networks

Reference 7

Resolution
unresolved
no resolver link, observed 2026-08-06T15:30:31.948943Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

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Observation 65f64ecf-4291-45f6-b7fe-d450daaa74fc · inbound

Hybrid Approaches for Black Hole Spin Estimation: From Classical Spectroscopy to Physics-Informed Machine Learning cites this paper.

Hybrid Approaches for Black Hole Spin Estimation: From Classical Spectroscopy to Physics-Informed Machine Learning Solving the Regge-Wheeler and Teukolsky equations: supervised versus unsupervised physics-informed neural networks

Reference 4

Resolution
unresolved
no resolver link, observed 2026-08-06T13:46:53.549092Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-06T13:46:53.549092Z digest=sha256:58be7ab260f9db43ea067668cbd5282d3e246c4d9978c8ffc1875a4768230047

Observation 1216513f-3c4f-431a-a667-72e8ce98fef0 · inbound

Physics informed operator learning of parameter dependent spectra cites this paper.

Physics informed operator learning of parameter dependent spectra Solving the Regge-Wheeler and Teukolsky equations: supervised versus unsupervised physics-informed neural networks

Reference 20

Resolution
verified exact
arxiv_id, observed 2026-05-11T21:26:14.497168Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

source=pdf_text observed=2026-05-08T05:38:52.477973Z digest=sha256:dbfc6dc35a68ae11c16a785fe07d5f97bb2b25dbb5be940469fb4889fa119315

Observation 34f5f58f-7430-49bc-aa56-b70d5eac6b18 · inbound

Odd-parity perturbations of trace-quadratic $f(R,T)$ black holes with anisotropic matter: admissible branches, axial ringdown, and a coupled-PINN benchmark cites this paper.

Odd-parity perturbations of trace-quadratic $f(R,T)$ black holes with anisotropic matter: admissible branches, axial ringdown, and a coupled-PINN benchmark Solving the Regge-Wheeler and Teukolsky equations: supervised versus unsupervised physics-informed neural networks

Reference 42

Resolution
verified exact
arxiv_id, observed 2026-07-02T04:26:36.266558Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

source=arxiv_source observed=2026-06-28T09:04:01.263448Z digest=sha256:59365eb5e4d9c9077380f1330a066d311c09c8d6df22bb2eab5cc4e267b4b074

Observation b454b011-ece3-4a3f-83ea-bf48a2d34114 · inbound

Beyond Data-Driven: How Physics-Informed Neural Networks are Reshaping Multi-Physics Design and Discovery cites this paper.

Beyond Data-Driven: How Physics-Informed Neural Networks are Reshaping Multi-Physics Design and Discovery Solving the Regge-Wheeler and Teukolsky equations: supervised versus unsupervised physics-informed neural networks

Reference 139

Resolution
verified exact
arxiv_id, observed 2026-07-04T08:19:44.090686Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

source=pdf_text observed=2026-06-26T11:55:25.831089Z digest=sha256:3bb8e4733efbadecf4511a5266afb386f715685fd2216461304aecbe62982db8