Typed states for the displayed outbound observations.
Source: paper_references, paper_reference_links
Paper Citation Record · LEDGER
High-Fidelity Machine Learning Approximations of Large-Scale Optimal Power Flow
As of 22 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 5 inbound Pith citation observations for arXiv:2006.16356.
A citation records a reference. It does not transfer a finding from one paper to another.
Coverage vector
One-hop event checks from named stored sources.
Source: scholarly_work_events, retraction_status_cache, observed 2026-08-22T06:32:14.747728+00:00
Pith citing papers itemized under the disclosed page cap.
Source: paper_references, paper_reference_links, observed 2026-08-15T21:00:12.515147Z
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 df686ae0-4367-49be-8c42-d7f01d9ce4ad · inbound
Large-scale Grid Optimization: The Workhorse of Future Grid Computations cites this paper.
Large-scale Grid Optimization: The Workhorse of Future Grid Computations High-Fidelity Machine Learning Approximations of Large-Scale Optimal Power Flow
Reference 108
- Resolution
- unresolved
- no resolver link, observed 2026-08-10T21:30:10.775770Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
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.
Observation 672b1d2b-9c98-4d29-9ab9-11fb1148abd6 · inbound
Sobolev Training of End-to-End Optimization Proxies cites this paper.
Sobolev Training of End-to-End Optimization Proxies High-Fidelity Machine Learning Approximations of Large-Scale Optimal Power Flow
Reference 46
- Resolution
- unresolved
- no resolver link, observed 2026-08-15T21:00:12.515147Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation 50c2131a-2fee-460a-b897-5ffd4f49dea1 · inbound
Quantum Hardware-in-the-Loop for Optimal Power Flow in Renewable-Integrated Power Systems cites this paper.
Quantum Hardware-in-the-Loop for Optimal Power Flow in Renewable-Integrated Power Systems High-Fidelity Machine Learning Approximations of Large-Scale Optimal Power Flow
Reference 17
- Resolution
- unresolved
- no resolver link, observed 2026-08-15T20:19:42.876423Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
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 2026-08-22T06:32:14.747728+00:00.