A Principled Framework to Evaluate Quality of AC-OPF Datasets for Machine Learning: Benchmarking a Novel, Scalable Generation Method | Paper Citation Record

Observation 0c60d7f2-ef6b-4d81-b636-5010cd9db04e · outbound

This paper cites An Efficient and Scalable Algo- rithm for the Creation of Representative Synthetic AC-OPF Datasets,.

A Principled Framework to Evaluate Quality of AC-OPF Datasets for Machine Learning: Benchmarking a Novel, Scalable Generation Method An Efficient and Scalable Algo- rithm for the Creation of Representative Synthetic AC-OPF Datasets,

Reference 1

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This paper cites Contribution ´a l’ ´etude du dispatching ´economique,.

A Principled Framework to Evaluate Quality of AC-OPF Datasets for Machine Learning: Benchmarking a Novel, Scalable Generation Method Contribution ´a l’ ´etude du dispatching ´economique,

Reference 2

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Observation 84c95c86-54db-4081-8e27-c8f91031cbf3 · outbound

This paper cites Zero duality gap in optimal power flow problem,.

A Principled Framework to Evaluate Quality of AC-OPF Datasets for Machine Learning: Benchmarking a Novel, Scalable Generation Method Zero duality gap in optimal power flow problem,

Reference 3

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This paper cites History of Optimal Power Flow and Formulations,.

A Principled Framework to Evaluate Quality of AC-OPF Datasets for Machine Learning: Benchmarking a Novel, Scalable Generation Method History of Optimal Power Flow and Formulations,

Reference 4

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This paper cites Critical review of recent advances and further devel- opments needed in ac optimal power flow,.

A Principled Framework to Evaluate Quality of AC-OPF Datasets for Machine Learning: Benchmarking a Novel, Scalable Generation Method Critical review of recent advances and further devel- opments needed in ac optimal power flow,

Reference 5

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This paper cites Learning warm-start points for ac optimal power flow,.

A Principled Framework to Evaluate Quality of AC-OPF Datasets for Machine Learning: Benchmarking a Novel, Scalable Generation Method Learning warm-start points for ac optimal power flow,

Reference 6

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This paper cites Smart-PGSim: Using Neural Network to Accelerate AC-OPF Power Grid Simulation,.

A Principled Framework to Evaluate Quality of AC-OPF Datasets for Machine Learning: Benchmarking a Novel, Scalable Generation Method Smart-PGSim: Using Neural Network to Accelerate AC-OPF Power Grid Simulation,

Reference 7

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This paper cites Initial estimate of ac optimal power flow with graph neural networks,.

A Principled Framework to Evaluate Quality of AC-OPF Datasets for Machine Learning: Benchmarking a Novel, Scalable Generation Method Initial estimate of ac optimal power flow with graph neural networks,

Reference 8

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This paper cites Leveraging Power Grid Topology in Machine Learning Assisted Optimal Power Flow,.

A Principled Framework to Evaluate Quality of AC-OPF Datasets for Machine Learning: Benchmarking a Novel, Scalable Generation Method Leveraging Power Grid Topology in Machine Learning Assisted Optimal Power Flow,

Reference 9

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This paper cites Hybrid learning aided inactive constraints filtering algorithm to enhance ac opf solution time,.

A Principled Framework to Evaluate Quality of AC-OPF Datasets for Machine Learning: Benchmarking a Novel, Scalable Generation Method Hybrid learning aided inactive constraints filtering algorithm to enhance ac opf solution time,

Reference 10

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This paper cites Learning optimal solutions for extremely fast ac optimal power flow,.

A Principled Framework to Evaluate Quality of AC-OPF Datasets for Machine Learning: Benchmarking a Novel, Scalable Generation Method Learning optimal solutions for extremely fast ac optimal power flow,

Reference 11

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This paper cites Predicting ac optimal power flows: Combining deep learning and lagrangian dual methods,.

A Principled Framework to Evaluate Quality of AC-OPF Datasets for Machine Learning: Benchmarking a Novel, Scalable Generation Method Predicting ac optimal power flows: Combining deep learning and lagrangian dual methods,

Reference 12

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This paper cites Optimal power flow using graph neural networks,.

A Principled Framework to Evaluate Quality of AC-OPF Datasets for Machine Learning: Benchmarking a Novel, Scalable Generation Method Optimal power flow using graph neural networks,

Reference 13

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This paper cites DC3: A learning method for optimization with hard constraints,.

A Principled Framework to Evaluate Quality of AC-OPF Datasets for Machine Learning: Benchmarking a Novel, Scalable Generation Method DC3: A learning method for optimization with hard constraints,

Reference 14

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Observation 4cb66cc8-4d67-42c8-89c3-776c5aa7fcc4 · outbound

This paper cites Physics-Informed Neural Net- works for AC Optimal Power Flow,.

A Principled Framework to Evaluate Quality of AC-OPF Datasets for Machine Learning: Benchmarking a Novel, Scalable Generation Method Physics-Informed Neural Net- works for AC Optimal Power Flow,

Reference 15

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Observation 4071aab7-5e0a-4dd0-891d-08a60c25bce4 · outbound

This paper cites Learning to Solve the AC-OPF Using Sensitivity-Informed Deep Neural Networks,.

A Principled Framework to Evaluate Quality of AC-OPF Datasets for Machine Learning: Benchmarking a Novel, Scalable Generation Method Learning to Solve the AC-OPF Using Sensitivity-Informed Deep Neural Networks,

Reference 16

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Observation d0a7013c-5c53-41cb-b6a4-e74d1a25c343 · outbound

This paper cites Deepopf: A feasibility- optimized deep neural network approach for ac optimal power flow problems,.

A Principled Framework to Evaluate Quality of AC-OPF Datasets for Machine Learning: Benchmarking a Novel, Scalable Generation Method Deepopf: A feasibility- optimized deep neural network approach for ac optimal power flow problems,

Reference 17

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Observation c8506bd4-33f5-43d2-85f0-a82a748c1a7e · outbound

This paper cites Topology-aware graph neural networks for learning feasible and adaptive ac-opf solutions,.

A Principled Framework to Evaluate Quality of AC-OPF Datasets for Machine Learning: Benchmarking a Novel, Scalable Generation Method Topology-aware graph neural networks for learning feasible and adaptive ac-opf solutions,

Reference 18

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Observation 63a58f0e-1a0d-4501-8a1c-20b7972609fa · outbound

This paper cites Unsupervised optimal power flow using graph neural networks,.

A Principled Framework to Evaluate Quality of AC-OPF Datasets for Machine Learning: Benchmarking a Novel, Scalable Generation Method Unsupervised optimal power flow using graph neural networks,

Reference 19

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Observation bc79f3d3-ad69-4f0b-9a34-38ea6400216e · outbound

This paper cites Optimal power flow with physics-informed typed graph neural networks,.

A Principled Framework to Evaluate Quality of AC-OPF Datasets for Machine Learning: Benchmarking a Novel, Scalable Generation Method Optimal power flow with physics-informed typed graph neural networks,

Reference 20

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Observation 96c3063d-c730-45ad-b7a4-7ff39739a67e · outbound

This paper cites CANOS: A Fast and Scalable Neural AC-OPF Solver Robust To N-1 Perturbations,.

A Principled Framework to Evaluate Quality of AC-OPF Datasets for Machine Learning: Benchmarking a Novel, Scalable Generation Method CANOS: A Fast and Scalable Neural AC-OPF Solver Robust To N-1 Perturbations,

Reference 21

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This paper cites Optimization Proxies using Limited Labeled Data and Training Time -- A Semi-Supervised Bayesian Neural Network Approach.

A Principled Framework to Evaluate Quality of AC-OPF Datasets for Machine Learning: Benchmarking a Novel, Scalable Generation Method Optimization Proxies using Limited Labeled Data and Training Time -- A Semi-Supervised Bayesian Neural Network Approach

Reference 22

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This paper cites Self-supervised primal-dual learning for constrained optimization,.

A Principled Framework to Evaluate Quality of AC-OPF Datasets for Machine Learning: Benchmarking a Novel, Scalable Generation Method Self-supervised primal-dual learning for constrained optimization,

Reference 23

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Observation 51bbda5e-feae-40c1-89df-125b2ab4f70e · outbound

This paper cites A learning-augmented approach for AC optimal power flow,.

A Principled Framework to Evaluate Quality of AC-OPF Datasets for Machine Learning: Benchmarking a Novel, Scalable Generation Method A learning-augmented approach for AC optimal power flow,

Reference 24

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This paper cites Topology-transferable physics-guided graph neural network for real-time optimal power flow,.

A Principled Framework to Evaluate Quality of AC-OPF Datasets for Machine Learning: Benchmarking a Novel, Scalable Generation Method Topology-transferable physics-guided graph neural network for real-time optimal power flow,

Reference 25

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Observation 09e0caf9-4f63-4954-b35c-1178224f8ec2 · outbound

This paper cites OPF-Learn: An Open- Source Framework for Creating Representative AC Optimal Power Flow Datasets,.

A Principled Framework to Evaluate Quality of AC-OPF Datasets for Machine Learning: Benchmarking a Novel, Scalable Generation Method OPF-Learn: An Open- Source Framework for Creating Representative AC Optimal Power Flow Datasets,

Reference 26

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This paper cites Scalable Bilevel Optimization for Gen- erating Maximally Representative OPF Datasets,.

A Principled Framework to Evaluate Quality of AC-OPF Datasets for Machine Learning: Benchmarking a Novel, Scalable Generation Method Scalable Bilevel Optimization for Gen- erating Maximally Representative OPF Datasets,

Reference 27

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This paper cites A large synthetic dataset for machine learning applications in power transmission grids,.

A Principled Framework to Evaluate Quality of AC-OPF Datasets for Machine Learning: Benchmarking a Novel, Scalable Generation Method A large synthetic dataset for machine learning applications in power transmission grids,

Reference 28

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Observation 55a4f0e5-70c0-4f79-b4f8-b5cf7d9ac10b · outbound

This paper cites Generating quality datasets for real-time security assessment: Balancing historically rele- vant and rare feasible operating conditions,.

A Principled Framework to Evaluate Quality of AC-OPF Datasets for Machine Learning: Benchmarking a Novel, Scalable Generation Method Generating quality datasets for real-time security assessment: Balancing historically rele- vant and rare feasible operating conditions,

Reference 29

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A Principled Framework to Evaluate Quality of AC-OPF Datasets for Machine Learning: Benchmarking a Novel, Scalable Generation Method Efficient creation of datasets for data-driven power system applications,

Reference 30

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This paper cites Split-based sequential sampling for realtime security assessment,.

A Principled Framework to Evaluate Quality of AC-OPF Datasets for Machine Learning: Benchmarking a Novel, Scalable Generation Method Split-based sequential sampling for realtime security assessment,

Reference 31

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This paper cites Enriching Neural Network Training Dataset to Improve Worst-Case Performance Guarantees,.

A Principled Framework to Evaluate Quality of AC-OPF Datasets for Machine Learning: Benchmarking a Novel, Scalable Generation Method Enriching Neural Network Training Dataset to Improve Worst-Case Performance Guarantees,

Reference 32

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Observation 1cd22a07-d557-40f6-8bfb-fa56f77f658b · outbound

This paper cites Optimal power flow based on physical-model-integrated neural network with worth-learning data generation,.

A Principled Framework to Evaluate Quality of AC-OPF Datasets for Machine Learning: Benchmarking a Novel, Scalable Generation Method Optimal power flow based on physical-model-integrated neural network with worth-learning data generation,

Reference 33

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Observation c03d1904-4ce4-442f-9aee-4fe7f2707b76 · outbound

This paper cites Generating high- quality datasets with critical state samples for data-driven power system applications,.

A Principled Framework to Evaluate Quality of AC-OPF Datasets for Machine Learning: Benchmarking a Novel, Scalable Generation Method Generating high- quality datasets with critical state samples for data-driven power system applications,

Reference 34

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This paper cites HEDGeOPF: High-quality, Efficient Dataset Genererator for the AC Optimal Power Flow,.

A Principled Framework to Evaluate Quality of AC-OPF Datasets for Machine Learning: Benchmarking a Novel, Scalable Generation Method HEDGeOPF: High-quality, Efficient Dataset Genererator for the AC Optimal Power Flow,

Reference 35

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Observation aeae4458-24dc-4be2-a49c-f931ce6d3f8f · outbound

This paper cites Vershynin, High-dimensional probability: An introduction with ap- plications in data science.

A Principled Framework to Evaluate Quality of AC-OPF Datasets for Machine Learning: Benchmarking a Novel, Scalable Generation Method Vershynin, High-dimensional probability: An introduction with ap- plications in data science

Reference 36

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This paper cites volesti: V olume Approximation and Sampling for Convex Polytopes in R,.

A Principled Framework to Evaluate Quality of AC-OPF Datasets for Machine Learning: Benchmarking a Novel, Scalable Generation Method volesti: V olume Approximation and Sampling for Convex Polytopes in R,

Reference 37

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This paper cites MATPOWER User’s Manual, Version 7.1.

A Principled Framework to Evaluate Quality of AC-OPF Datasets for Machine Learning: Benchmarking a Novel, Scalable Generation Method MATPOWER User’s Manual, Version 7.1

Reference 38

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Observation ce0e5b54-885c-432c-bfce-19de0db93bdc · outbound

This paper cites Powermodels.jl: An open-source framework for exploring power flow formulations,.

A Principled Framework to Evaluate Quality of AC-OPF Datasets for Machine Learning: Benchmarking a Novel, Scalable Generation Method Powermodels.jl: An open-source framework for exploring power flow formulations,

Reference 39

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This paper cites JuMP 1.0: Recent improvements to a modeling language for mathematical optimization,.

A Principled Framework to Evaluate Quality of AC-OPF Datasets for Machine Learning: Benchmarking a Novel, Scalable Generation Method JuMP 1.0: Recent improvements to a modeling language for mathematical optimization,

Reference 40

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T16:02:32.499762Z

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Observation b434883f-c215-4211-869a-b082c5606703 · outbound

This paper cites Delving into Deep Imbalanced Regression,.

A Principled Framework to Evaluate Quality of AC-OPF Datasets for Machine Learning: Benchmarking a Novel, Scalable Generation Method Delving into Deep Imbalanced Regression,

Reference 41

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T16:02:32.482409Z

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Observation 2a681233-c8fc-41cf-a96b-1899d1c07455 · outbound

This paper cites Generalized Simpson-diversity,.

A Principled Framework to Evaluate Quality of AC-OPF Datasets for Machine Learning: Benchmarking a Novel, Scalable Generation Method Generalized Simpson-diversity,

Reference 42

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T16:02:32.466601Z

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Observation 55dda73a-ef9e-40cc-82d3-05b74ec05442 · outbound

This paper cites Identifying Redundant Constraints for AC OPF: The Challenges of Local Solutions, Relaxation Tightness, and Approximation Inaccuracy,.

A Principled Framework to Evaluate Quality of AC-OPF Datasets for Machine Learning: Benchmarking a Novel, Scalable Generation Method Identifying Redundant Constraints for AC OPF: The Challenges of Local Solutions, Relaxation Tightness, and Approximation Inaccuracy,

Reference 43

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Observation 888fb5fd-5148-4943-9d38-127ff947a9a2 · outbound

This paper cites OPFData: Large-scale datasets for AC optimal power flow with topological perturbations,.

A Principled Framework to Evaluate Quality of AC-OPF Datasets for Machine Learning: Benchmarking a Novel, Scalable Generation Method OPFData: Large-scale datasets for AC optimal power flow with topological perturbations,

Reference 44

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T16:02:32.436268Z

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Observation e1d958c3-b525-4045-b235-3782f3bc38f4 · outbound

This paper cites The Power Grid Library for Benchmarking AC Optimal Power Flow Algorithms.

A Principled Framework to Evaluate Quality of AC-OPF Datasets for Machine Learning: Benchmarking a Novel, Scalable Generation Method The Power Grid Library for Benchmarking AC Optimal Power Flow Algorithms

Reference 45

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Observation 9ea47b6f-57b4-4c6c-bd1d-e49629cb2586 · outbound

This paper cites OPFData: Large-scale datasets for AC optimal power flow with topological perturbations.

A Principled Framework to Evaluate Quality of AC-OPF Datasets for Machine Learning: Benchmarking a Novel, Scalable Generation Method OPFData: Large-scale datasets for AC optimal power flow with topological perturbations

Reference 46

Resolution: unresolved; no resolver link, observed 2026-08-05T16:02:32.269644Z

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Unavailable: canonical work link unavailable.

Observation 6eeae6a3-3ade-4803-92eb-e75d838e6204 · outbound

This paper cites an unresolved cited work.

A Principled Framework to Evaluate Quality of AC-OPF Datasets for Machine Learning: Benchmarking a Novel, Scalable Generation Method Unresolved cited work

Reference 47

Resolution: unresolved; raw_fallback, observed 2026-08-05T16:02:32.401809Z

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Observation 040bc9ff-9e42-4d22-aa15-0f6b3628d119 · outbound

This paper cites OPFLearn.jl,.

A Principled Framework to Evaluate Quality of AC-OPF Datasets for Machine Learning: Benchmarking a Novel, Scalable Generation Method OPFLearn.jl,

Reference 48

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T16:02:32.418392Z

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.

Observation 808f2391-8771-43cc-906c-bf698c7d6dfa · outbound

This paper cites On the implementation of an interior- point filter line-search algorithm for large-scale nonlinear programming,.

A Principled Framework to Evaluate Quality of AC-OPF Datasets for Machine Learning: Benchmarking a Novel, Scalable Generation Method On the implementation of an interior- point filter line-search algorithm for large-scale nonlinear programming,

Reference 50

Resolution: unresolved; no resolver link, observed 2026-08-05T16:02:32.287714Z

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Unavailable: canonical work link unavailable.

Observation 207898e9-c24a-4388-8b1a-b2ef71e08823 · outbound

This paper cites CANOS: A Fast and Scalable Neural AC-OPF Solver Robust To N-1 Perturbations.

A Principled Framework to Evaluate Quality of AC-OPF Datasets for Machine Learning: Benchmarking a Novel, Scalable Generation Method CANOS: A Fast and Scalable Neural AC-OPF Solver Robust To N-1 Perturbations

Reference 2024

Resolution: unresolved; no resolver link, observed 2026-08-05T16:02:32.161178Z

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Unavailable: canonical work link unavailable.

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