pith. sign in

Integrity report for Engineering Hybrid Physics-Informed Neural Networks for Next-Generation Electricity Systems: A State-of-the-Art Review

A machine-verified record of the checks Pith has run against this paper: detector runs, findings, signed bundle events, and canonical identifiers.

arXiv:2605.21903 · pith:2026:7LMMHE2PFTFM6IGSQOG737HKZ7

0Critical
0Advisory
7Detectors run
2026-06-05Last checked

Paper page arXiv integrity.json bundle.json

Detector runs

doi_title_agreement completed v1.0.0 · findings 0 · 2026-06-05 09:06:09.887480+00:00
doi_compliance completed v1.0.0 · findings 0 · 2026-06-02 11:22:15.223719+00:00
ai_meta_artifact completed v1.0.0 · findings 0 · 2026-05-29 05:43:17.913497+00:00
claim_evidence completed v1.0.0 · findings 0 · 2026-05-27 17:04:31.836304+00:00
shingle_duplication completed v0.1.0 · findings 0 · 2026-05-25 01:50:16.727965+00:00
citation_quote_validity completed v0.1.0 · findings 0 · 2026-05-24 01:50:54.240787+00:00
cited_work_retraction completed v1.0.0 · findings 0 · 2026-05-22 16:22:50.403081+00:00

Findings

No public integrity findings for this paper.

Signed record

The machine-readable record for this paper lives at /pith/7LMMHE2PFTFM6IGSQOG737HKZ7/integrity.json. Pith Number bundles also include signed pith.integrity.v1 events where a Pith Number exists.