Paper Citation Record · LEDGER

Toolbox for Developing Physics Informed Neural Networks for Power Systems Components

As of 15 August 2026, this Paper Citation Record lists 20 of 20 outbound references and 0 inbound Pith citation observations for arXiv:2502.06412.

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

pith.paper-citation-record.v1
2502.06412 v1

Coverage vector

measured 20 of 20 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-08T15:36:54.952337Z

measured 20 of 20 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-14T06:32:32.682623+00:00

measured 0 of 0 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links

measured 0 of 1 external citation measurements

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

Source: cited_works

Reference resolution

20 of 20 outbound references displayed

verified exact1
verified fuzzy13
unresolved6
parse uncertain0
malformed identifier0
metadata mismatch0

External citation measurements

No source-named external measurement is stored.

Outbound references

Observation 03097cda-3f22-4198-a918-fbdea938aace · outbound

This paper cites A critical review of the integration of renewable energy sources with various technologies,.

Toolbox for Developing Physics Informed Neural Networks for Power Systems Components A critical review of the integration of renewable energy sources with various technologies,

Reference 1

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T15:36:55.300289Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation fb0366b0-f9b3-44ed-a153-1fcd9b57cdd4 · outbound

This paper cites A review of machine learning approaches to power system security and stability,.

Toolbox for Developing Physics Informed Neural Networks for Power Systems Components A review of machine learning approaches to power system security and stability,

Reference 2

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T15:36:55.284982Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation 00ea3a37-350a-4cf9-ad94-046494951380 · outbound

This paper cites an unresolved cited work.

Toolbox for Developing Physics Informed Neural Networks for Power Systems Components Unresolved cited work

Reference 3

Resolution: unresolved; raw_fallback, observed 2026-08-08T15:36:55.270499Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation 6e40aa7d-6b24-411e-af4c-8ff3b4e56712 · outbound

This paper cites Interpretable machine learning for power sys- tems: Establishing confidence in shapley additive explanations,.

Toolbox for Developing Physics Informed Neural Networks for Power Systems Components Interpretable machine learning for power sys- tems: Establishing confidence in shapley additive explanations,

Reference 4

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T15:36:55.255149Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation 345a2f1e-9644-45b5-9dd7-e39d44224323 · outbound

This paper cites Artificial neural networks for solving ordinary and partial differential equations,.

Toolbox for Developing Physics Informed Neural Networks for Power Systems Components Artificial neural networks for solving ordinary and partial differential equations,

Reference 5

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T15:36:55.237202Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation ceff0c80-416a-4b53-b7d7-92d5c999ec69 · outbound

This paper cites Physics-informed neural networks: A deep learning framework for solving forward and inverse problems involving nonlinear partial differential equations,.

Toolbox for Developing Physics Informed Neural Networks for Power Systems Components Physics-informed neural networks: A deep learning framework for solving forward and inverse problems involving nonlinear partial differential equations,

Reference 6

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T15:36:55.219544Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation 46850186-98ab-4520-8b15-0516d18eb54b · outbound

This paper cites Applications of physics-informed neural networks in power systems - a review,.

Toolbox for Developing Physics Informed Neural Networks for Power Systems Components Applications of physics-informed neural networks in power systems - a review,

Reference 7

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T15:36:55.201462Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation 51f2e07a-94ef-4fcf-ac97-7ebe700a90a1 · outbound

This paper cites Transient Stability Analysis with Physics-Informed Neural Networks.

Toolbox for Developing Physics Informed Neural Networks for Power Systems Components Transient Stability Analysis with Physics-Informed Neural Networks

Reference 8

Resolution: unresolved; no resolver link, observed 2026-08-08T15:36:54.891131Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 487e6dbf-1724-4e16-b94c-5a42bd31314f · outbound

This paper cites Physics-Informed Graphical Neural Network for Parameter & State Estimations in Power Systems.

Toolbox for Developing Physics Informed Neural Networks for Power Systems Components Physics-Informed Graphical Neural Network for Parameter & State Estimations in Power Systems

Reference 9

Resolution: unresolved; no resolver link, observed 2026-08-08T15:36:54.896637Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 79c5a37d-f218-48f4-80b9-aab4d0a85063 · outbound

This paper cites Integrating physics- informed neural networks into power system dynamic simulations,.

Toolbox for Developing Physics Informed Neural Networks for Power Systems Components Integrating physics- informed neural networks into power system dynamic simulations,

Reference 10

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T15:36:55.184195Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation 77ad3b8d-db82-4d3f-ab94-64cc571c7910 · outbound

This paper cites Pinnsim: A simulator for power system dynamics based on physics-informed neural networks,.

Toolbox for Developing Physics Informed Neural Networks for Power Systems Components Pinnsim: A simulator for power system dynamics based on physics-informed neural networks,

Reference 11

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T15:36:55.168433Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation f015e9e4-48ef-42a9-8b3c-3a7834aae163 · outbound

This paper cites Approximation capabilities of multilayer feedforward net- works,.

Toolbox for Developing Physics Informed Neural Networks for Power Systems Components Approximation capabilities of multilayer feedforward net- works,

Reference 12

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T15:36:55.150859Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation 9fabc1ea-6c66-401b-b524-615dddfada69 · outbound

This paper cites A comparison of three methods for selecting values of input variables in the analysis of output from a computer code,.

Toolbox for Developing Physics Informed Neural Networks for Power Systems Components A comparison of three methods for selecting values of input variables in the analysis of output from a computer code,

Reference 13

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T15:36:55.135581Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation 64475068-c841-4249-936c-c67c8026c493 · outbound

This paper cites Physics-informed machine learning for power system dynamics: A framework incorporating trustworthiness,.

Toolbox for Developing Physics Informed Neural Networks for Power Systems Components Physics-informed machine learning for power system dynamics: A framework incorporating trustworthiness,

Reference 14

Resolution: verified exact; doi, observed 2026-08-08T15:36:55.003734Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation 230690de-7420-4335-88a1-7fffaa2c0f1e · outbound

This paper cites SoftAdapt: Techniques for Adaptive Loss Weighting of Neural Networks with Multi-Part Loss Functions.

Toolbox for Developing Physics Informed Neural Networks for Power Systems Components SoftAdapt: Techniques for Adaptive Loss Weighting of Neural Networks with Multi-Part Loss Functions

Reference 15

Resolution: unresolved; no resolver link, observed 2026-08-08T15:36:54.930872Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation f91b3853-5c75-4df6-ad7b-7bdc75a376b2 · outbound

This paper cites Pytorch,.

Toolbox for Developing Physics Informed Neural Networks for Power Systems Components Pytorch,

Reference 16

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T15:36:55.119473Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation f311c94b-10b7-444f-b5b7-5e8e0ebc7265 · outbound

This paper cites On the limited memory bfgs method for large scale optimization,.

Toolbox for Developing Physics Informed Neural Networks for Power Systems Components On the limited memory bfgs method for large scale optimization,

Reference 17

Resolution: unresolved; no resolver link, observed 2026-08-08T15:36:54.940806Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation c40726fe-389b-47bd-9a01-1af3d6345e9c · outbound

This paper cites Experiment tracking with weights and biases,.

Toolbox for Developing Physics Informed Neural Networks for Power Systems Components Experiment tracking with weights and biases,

Reference 18

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T15:36:55.104033Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation 79ba65b2-f853-452a-8f39-b04e870cfd4a · outbound

This paper cites PINNACLE: PINN adaptive collocation and experimental points selection,.

Toolbox for Developing Physics Informed Neural Networks for Power Systems Components PINNACLE: PINN adaptive collocation and experimental points selection,

Reference 19

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T15:36:55.088636Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation d9827894-e151-4fd1-af0b-1b2f6b5c2977 · outbound

This paper cites Physics-Informed Neural Networks: a Plug and Play Integration into Power System Dynamic Simulations.

Toolbox for Developing Physics Informed Neural Networks for Power Systems Components Physics-Informed Neural Networks: a Plug and Play Integration into Power System Dynamic Simulations

Reference 2024

Resolution: unresolved; no resolver link, observed 2026-08-08T15:36:54.906154Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Pith citing papers

No inbound Pith citation observations are available.