Pith. sign in

Paper Citation Record · LEDGER

Machine Learning for AC Optimal Power Flow

As of 10 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 2 inbound Pith citation observations for arXiv:1910.08842.

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

pith.paper-citation-record.v1
1910.08842 v1

Coverage vector

measured 0 of 0 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links

measured 2 of 2 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-10T06:31:04.303077+00:00

measured 2 of 2 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-09T19:36:57.886203Z

measured 0 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-07-02T01:36:26.318469Z

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 b837bb0c-8008-477b-af53-017dc11481f2 · inbound

HoP: Homeomorphic Polar Learning for Hard Constrained Optimization cites this paper.

HoP: Homeomorphic Polar Learning for Hard Constrained Optimization Machine Learning for AC Optimal Power Flow

Reference 10

Resolution
unresolved
no resolver link, observed 2026-08-09T19:36:57.886203Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-09T19:36:57.886203Z digest=sha256:c4d2feef41fa2e72b0e8db823e2e846a091e212ee48c7db7f6c22b0fbf142d4f

Observation 60c178c7-4705-4f71-bdc7-a098cd6e1681 · inbound

Rethinking Neural Width for Alternating Current Optimal Power Flow Proxies cites this paper.

Rethinking Neural Width for Alternating Current Optimal Power Flow Proxies Machine Learning for AC Optimal Power Flow

Reference 2

Resolution
verified exact
arxiv_id, observed 2026-07-02T01:36:26.321348Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

source=pdf_text observed=2026-06-28T11:36:55.493267Z digest=sha256:24131d49a73d68f2d3809ef05c4c3d614bdfa5c4468e792b83ce43ed7580bdbe