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Paper Citation Record · LEDGER

High-Fidelity Machine Learning Approximations of Large-Scale Optimal Power Flow

As of 8 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 2 inbound Pith citation observations for arXiv:2006.16356.

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

pith.paper-citation-record.v1
2006.16356 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 2026-08-08T06:32:00.761636+00:00

measured 2 of 2 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-07T18:37:52.046834Z

measured 0 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-05-18T08:51:08.880764Z

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 2fe92e2b-49e4-4c57-8f3d-4a0f919dc109 · inbound

Diffusion-based learning framework for Constrained Nonconvex Optimization with Weighted Bootstrapped Refinement cites this paper.

Diffusion-based learning framework for Constrained Nonconvex Optimization with Weighted Bootstrapped Refinement High-Fidelity Machine Learning Approximations of Large-Scale Optimal Power Flow

Reference 8

Resolution
unresolved
no resolver link, observed 2026-08-07T18:37:52.046834Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-07T18:37:52.046834Z digest=sha256:1879fa87d3af92f296208565b9277c0a935d858c72fd7ff7f441c601b98fa712

Observation 2bc19eab-3844-470d-b127-1da614fd9aea · inbound

Towards Generalization of Graph Neural Networks for AC Optimal Power Flow cites this paper.

Towards Generalization of Graph Neural Networks for AC Optimal Power Flow High-Fidelity Machine Learning Approximations of Large-Scale Optimal Power Flow

Reference 9

Resolution
verified exact
arxiv_id, observed 2026-05-18T08:51:08.882876Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

source=pdf_text observed=2026-05-18T08:49:17.172538Z digest=sha256:faaec0066c3bb6de36afc5bfa9374702d0274672e8e793b1445c5b4debb054eb