Paper Citation Record · LEDGER

Learning Constraint Surrogate Model for Two-stage Stochastic Unit Commitment

As of 19 August 2026, this Paper Citation Record lists 29 of 29 outbound references and 1 inbound Pith citation observation for arXiv:2509.10246.

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

pith.paper-citation-record.v1
2509.10246 v1

Coverage vector

measured 29 of 29 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-15T16:06:39.394396Z

measured 30 of 30 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-19T06:32:44.657259+00:00

measured 1 of 1 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-04T06:27:27.782964Z

measured 0 of 1 external citation measurements

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

Source: cited_works

Reference resolution

29 of 29 outbound references displayed

verified exact1
verified fuzzy21
unresolved7
parse uncertain0
malformed identifier0
metadata mismatch0

External citation measurements

No source-named external measurement is stored.

Outbound references

Observation cafe14e7-5dde-4388-a056-cafa6251be21 · outbound

This paper cites an unresolved cited work.

Learning Constraint Surrogate Model for Two-stage Stochastic Unit Commitment Unresolved cited work

Reference 1

Resolution: unresolved; no resolver link, observed 2026-08-15T16:06:39.264216Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation ba789015-e771-49dd-84a4-186571704a5b · outbound

This paper cites Unit commitment-a bibliographical survey,.

Learning Constraint Surrogate Model for Two-stage Stochastic Unit Commitment Unit commitment-a bibliographical survey,

Reference 2

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T16:06:39.854072Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation 3fd70a95-dfd0-4efb-b35a-d0f013ec10d9 · outbound

This paper cites an unresolved cited work.

Learning Constraint Surrogate Model for Two-stage Stochastic Unit Commitment Unresolved cited work

Reference 3

Resolution: unresolved; raw_fallback, observed 2026-08-15T16:06:39.837135Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation 40d68850-7bd4-430c-ae4e-a92e9e980030 · outbound

This paper cites Large-scale unit commitment under uncertainty: an updated literature survey,.

Learning Constraint Surrogate Model for Two-stage Stochastic Unit Commitment Large-scale unit commitment under uncertainty: an updated literature survey,

Reference 4

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T16:06:39.822108Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation cb9a1f13-995f-42fb-b984-a007f6d999d0 · outbound

This paper cites Stochastic and deterministic unit commitment considering uncertainty and variability reserves for high renewable integration,.

Learning Constraint Surrogate Model for Two-stage Stochastic Unit Commitment Stochastic and deterministic unit commitment considering uncertainty and variability reserves for high renewable integration,

Reference 5

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T16:06:39.806173Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation 476671b0-0f08-4af2-ba50-c2a2b16e3ac3 · outbound

This paper cites Adaptive robust optimization for the security constrained unit commitment problem,.

Learning Constraint Surrogate Model for Two-stage Stochastic Unit Commitment Adaptive robust optimization for the security constrained unit commitment problem,

Reference 6

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T16:06:39.789979Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation 0d11e5d7-1580-4d02-8ea5-ce0a2812d945 · outbound

This paper cites A robust approach to chance constrained optimal power flow with renewable generation,.

Learning Constraint Surrogate Model for Two-stage Stochastic Unit Commitment A robust approach to chance constrained optimal power flow with renewable generation,

Reference 7

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T16:06:39.774332Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation d8b54901-e69c-4f4b-b050-4b79686e596e · outbound

This paper cites A stochastic model for the unit commitment problem,.

Learning Constraint Surrogate Model for Two-stage Stochastic Unit Commitment A stochastic model for the unit commitment problem,

Reference 8

Resolution: unresolved; no resolver link, observed 2026-08-15T16:06:39.299189Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation a150ef08-4785-4022-8122-bbd7094a7d6f · outbound

This paper cites Stochastic unit commitment problem,.

Learning Constraint Surrogate Model for Two-stage Stochastic Unit Commitment Stochastic unit commitment problem,

Reference 9

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T16:06:39.749021Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation 5ecc9ac8-1279-4745-bcff-b3620c2c43de · outbound

This paper cites an unresolved cited work.

Learning Constraint Surrogate Model for Two-stage Stochastic Unit Commitment Unresolved cited work

Reference 10

Resolution: unresolved; raw_fallback, observed 2026-08-15T16:06:39.734163Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation e735ce0c-dab9-4956-aa7f-619d160c82d1 · outbound

This paper cites an unresolved cited work.

Learning Constraint Surrogate Model for Two-stage Stochastic Unit Commitment Unresolved cited work

Reference 11

Resolution: unresolved; raw_fallback, observed 2026-08-15T16:06:39.719101Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation 7de83596-f242-46f6-8c01-1c5a7b76f868 · outbound

This paper cites Large-scale unit commitment under uncertainty,.

Learning Constraint Surrogate Model for Two-stage Stochastic Unit Commitment Large-scale unit commitment under uncertainty,

Reference 12

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T16:06:39.703549Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation bb87ad20-52e9-43ad-aaac-8ea855f08343 · outbound

This paper cites Distributed optimization and statistical learning via the alternating direction method of multipliers,.

Learning Constraint Surrogate Model for Two-stage Stochastic Unit Commitment Distributed optimization and statistical learning via the alternating direction method of multipliers,

Reference 13

Resolution: unresolved; no resolver link, observed 2026-08-15T16:06:39.321571Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation d18a6d55-1465-4775-8dce-3c25df4469ad · outbound

This paper cites Optimization based methods for unit commitment: Lagrangian relaxation versus general mixed integer programming,.

Learning Constraint Surrogate Model for Two-stage Stochastic Unit Commitment Optimization based methods for unit commitment: Lagrangian relaxation versus general mixed integer programming,

Reference 14

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T16:06:39.677139Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation dfc1c5dd-a43a-4e5c-9d62-7812cc507e5b · outbound

This paper cites Learning optimal power flow: Worst-case guarantees for neural networks,.

Learning Constraint Surrogate Model for Two-stage Stochastic Unit Commitment Learning optimal power flow: Worst-case guarantees for neural networks,

Reference 15

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T16:06:39.662067Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation 05181b87-ff00-4a05-ad92-7e9b560c6813 · outbound

This paper cites A review of machine learning applications in power system resilience,.

Learning Constraint Surrogate Model for Two-stage Stochastic Unit Commitment A review of machine learning applications in power system resilience,

Reference 16

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T16:06:39.647040Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation 5aa0371c-9326-4427-a773-d1c4eacf64e5 · outbound

This paper cites Interpretable Machine Learning for Power Systems: Establishing Confidence in SHapley Additive exPlanations.

Learning Constraint Surrogate Model for Two-stage Stochastic Unit Commitment Interpretable Machine Learning for Power Systems: Establishing Confidence in SHapley Additive exPlanations

Reference 17

Resolution: verified exact; local_arxiv, observed 2026-08-15T16:06:39.440302Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation ea52c9e7-2a49-4958-b967-9d5f4ae4b960 · outbound

This paper cites Using mixed integer programming to solve power grid blackout problems,.

Learning Constraint Surrogate Model for Two-stage Stochastic Unit Commitment Using mixed integer programming to solve power grid blackout problems,

Reference 18

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T16:06:39.631107Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation 737484c5-c771-4f1f-9f4c-989264ebfc93 · outbound

This paper cites A new generation of ai: A review and perspective on machine learning technologies applied to smart energy and electric power systems,.

Learning Constraint Surrogate Model for Two-stage Stochastic Unit Commitment A new generation of ai: A review and perspective on machine learning technologies applied to smart energy and electric power systems,

Reference 19

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T16:06:39.614164Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation 351aa6e0-7a85-42d5-aca2-b2d6b252689d · outbound

This paper cites Power system reduction techniques for planning and stability studies: A review,.

Learning Constraint Surrogate Model for Two-stage Stochastic Unit Commitment Power system reduction techniques for planning and stability studies: A review,

Reference 20

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T16:06:39.598224Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation 5eaf5cc2-57e5-417c-a540-b21343893c7a · outbound

This paper cites Explicit data-driven small-signal stability constrained optimal power flow,.

Learning Constraint Surrogate Model for Two-stage Stochastic Unit Commitment Explicit data-driven small-signal stability constrained optimal power flow,

Reference 21

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T16:06:39.581988Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation 9538c9b1-a16f-4a33-89c1-b272ab122528 · outbound

This paper cites Ai feynman: A physics-inspired method for symbolic regression,.

Learning Constraint Surrogate Model for Two-stage Stochastic Unit Commitment Ai feynman: A physics-inspired method for symbolic regression,

Reference 22

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T16:06:39.566222Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation 0d0daa20-cf41-4484-8300-a61cab6e6a8b · outbound

This paper cites A review on symbolic regression in power systems: Methods, applications, and future directions,.

Learning Constraint Surrogate Model for Two-stage Stochastic Unit Commitment A review on symbolic regression in power systems: Methods, applications, and future directions,

Reference 23

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T16:06:39.550693Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation b846066a-6c30-43ce-a739-1ac2a96861b4 · outbound

This paper cites Discovering governing equations from data by sparse identification of nonlinear dynamical systems,.

Learning Constraint Surrogate Model for Two-stage Stochastic Unit Commitment Discovering governing equations from data by sparse identification of nonlinear dynamical systems,

Reference 24

Resolution: unresolved; no resolver link, observed 2026-08-15T16:06:39.371567Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 5db097af-f367-4db1-8a3b-dfb1cd7db6ef · outbound

This paper cites Unit commitment predictor with a performance guarantee: A support vector machine classifier,.

Learning Constraint Surrogate Model for Two-stage Stochastic Unit Commitment Unit commitment predictor with a performance guarantee: A support vector machine classifier,

Reference 25

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T16:06:39.522439Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation c90d09fc-641d-40db-b8f7-2ef399b82e8c · outbound

This paper cites The interplay of optimization and machine learning research,.

Learning Constraint Surrogate Model for Two-stage Stochastic Unit Commitment The interplay of optimization and machine learning research,

Reference 26

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T16:06:39.505662Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation be3376a9-df45-4ec4-8a83-951f227fc2ad · outbound

This paper cites Joint chance constraints in ac optimal power flow: Improving bounds through learning,.

Learning Constraint Surrogate Model for Two-stage Stochastic Unit Commitment Joint chance constraints in ac optimal power flow: Improving bounds through learning,

Reference 27

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T16:06:39.489340Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation e07830f7-c1a7-4343-8a44-ba7e295a2230 · outbound

This paper cites Accelerating l-shaped two-stage stochastic scuc with learning integrated benders decomposition,.

Learning Constraint Surrogate Model for Two-stage Stochastic Unit Commitment Accelerating l-shaped two-stage stochastic scuc with learning integrated benders decomposition,

Reference 28

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T16:06:39.472526Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Observation dba1f987-ed90-45cb-a09f-359e36d014c8 · outbound

This paper cites A tutorial on support vector machines for pattern recognition,.

Learning Constraint Surrogate Model for Two-stage Stochastic Unit Commitment A tutorial on support vector machines for pattern recognition,

Reference 29

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T16:06:39.456602Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

Pith citing papers

Observation 12a05159-b728-43e7-b96e-fe973627f750 · inbound

A Survey on Applications of Quantum Computing for Unit Commitment cites this paper.

A Survey on Applications of Quantum Computing for Unit Commitment Learning Constraint Surrogate Model for Two-stage Stochastic Unit Commitment

Reference 5

Resolution: unresolved; no resolver link, observed 2026-08-04T06:27:27.782964Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.