Paper Citation Record · LEDGER

Physics-Informed Neural Networks for Electrical Circuit Analysis: Applications in Dielectric Material Modeling

As of 13 August 2026, this Paper Citation Record lists 9 of 9 outbound references and 1 inbound Pith citation observation for arXiv:2411.10483.

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

pith.paper-citation-record.v1
2411.10483 v1

Coverage vector

measured 9 of 9 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-12T21:15:08.802827Z

measured 10 of 10 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-13T06:32:02.005865+00:00

measured 1 of 1 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-04T12:43:33.827678Z

measured 0 of 1 external citation measurements

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

Source: cited_works

Reference resolution

9 of 9 outbound references displayed

verified exact0
verified fuzzy6
unresolved3
parse uncertain0
malformed identifier0
metadata mismatch0

External citation measurements

No source-named external measurement is stored.

Outbound references

Observation 69fabd97-2c0b-4a0c-ab98-3bfd439105a4 · outbound

This paper cites Zaenglaengl, ”Dielectric spectroscopy in time and frequency domain for HV power equipment.

Physics-Informed Neural Networks for Electrical Circuit Analysis: Applications in Dielectric Material Modeling Zaenglaengl, ”Dielectric spectroscopy in time and frequency domain for HV power equipment

Reference 1

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T21:15:09.056041Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

Observation 4c16dcb4-52a5-488b-8a35-b8396e0248a8 · outbound

This paper cites Kremer and A.

Physics-Informed Neural Networks for Electrical Circuit Analysis: Applications in Dielectric Material Modeling Kremer and A

Reference 2

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T21:15:09.040944Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

Observation 5f370fbc-181e-46cb-b3bd-88e761da8fa5 · outbound

This paper cites Workshop Report on Basic Research Needs for Scientific Machine Learning: Core Technologies for Artificial Intelligence.

Physics-Informed Neural Networks for Electrical Circuit Analysis: Applications in Dielectric Material Modeling Workshop Report on Basic Research Needs for Scientific Machine Learning: Core Technologies for Artificial Intelligence

Reference 3

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T21:15:09.025416Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

Observation d502c838-686c-43e3-8a38-17d0c4708649 · outbound

This paper cites Machine learning and big scien- tific data.

Physics-Informed Neural Networks for Electrical Circuit Analysis: Applications in Dielectric Material Modeling Machine learning and big scien- tific data

Reference 4

Resolution: unresolved; no resolver link, observed 2026-08-12T21:15:08.780129Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 8fc812e4-34ba-4c8a-8be7-a7df7663d0b9 · outbound

This paper cites Scientific Machine Learning through Physics-Informed Neural Networks: Where we are and What’s next.

Physics-Informed Neural Networks for Electrical Circuit Analysis: Applications in Dielectric Material Modeling Scientific Machine Learning through Physics-Informed Neural Networks: Where we are and What’s next

Reference 5

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T21:15:09.010997Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

Observation 1725626e-90d9-4c40-aea4-b6b959680466 · outbound

This paper cites an unresolved cited work.

Physics-Informed Neural Networks for Electrical Circuit Analysis: Applications in Dielectric Material Modeling Unresolved cited work

Reference 6

Resolution: unresolved; raw_fallback, observed 2026-08-12T21:15:08.995703Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

Observation 40fd41b4-fd99-43a1-bd94-8f5e6344df4d · outbound

This paper cites GAMM-Mitteilungen 44(2), e202100,006 (2021).

Physics-Informed Neural Networks for Electrical Circuit Analysis: Applications in Dielectric Material Modeling GAMM-Mitteilungen 44(2), e202100,006 (2021)

Reference 7

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T21:15:08.981536Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

Observation e98eed59-803b-405a-b554-d12228250668 · outbound

This paper cites an unresolved cited work.

Physics-Informed Neural Networks for Electrical Circuit Analysis: Applications in Dielectric Material Modeling Unresolved cited work

Reference 8

Resolution: unresolved; no resolver link, observed 2026-08-12T21:15:08.798618Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation b7ad2743-627b-4ec0-bf89-3a553b9954f4 · outbound

This paper cites Deep Learning.

Physics-Informed Neural Networks for Electrical Circuit Analysis: Applications in Dielectric Material Modeling Deep Learning

Reference 9

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T21:15:08.965592Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.

Pith citing papers

Observation 912c3107-2924-4166-a3d1-eff0f38fbaed · inbound

A physics-informed neural network approach to the point defect model for electrochemical oxide film growth cites this paper.

A physics-informed neural network approach to the point defect model for electrochemical oxide film growth Physics-Informed Neural Networks for Electrical Circuit Analysis: Applications in Dielectric Material Modeling

Reference 19

Resolution: unresolved; no resolver link, observed 2026-08-04T12:43:33.827678Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.