Paper Citation Record · LEDGER

Constrained Diffusion Models for Synthesizing Representative Power Flow Datasets

As of 22 August 2026, this Paper Citation Record lists 47 of 47 outbound references and 3 inbound Pith citation observations for arXiv:2506.11281.

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

pith.paper-citation-record.v1
2506.11281 v2

Coverage vector

measured 47 of 47 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-07T04:19:06.946738Z

measured 50 of 50 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-21T06:32:19.484+00:00

measured 3 of 3 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-07T04:19:03.188527Z

measured 1 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-08-05T02:28:24.338817Z

Reference resolution

47 of 47 outbound references displayed

verified exact2
verified fuzzy36
unresolved9
parse uncertain0
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External citation measurements

0
arxiv_reference, observed 2026-08-05T02:28:24.338817Z

Outbound references

Observation 1c2b56e8-bc41-4e58-97cd-dae2e0f96364 · outbound

This paper cites PGLearn -- An Open-Source Learning Toolkit for Optimal Power Flow.

Constrained Diffusion Models for Synthesizing Representative Power Flow Datasets PGLearn -- An Open-Source Learning Toolkit for Optimal Power Flow

Reference 1

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Observation fd45d05b-8dc7-4647-b629-f2286ed044e6 · outbound

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

Constrained Diffusion Models for Synthesizing Representative Power Flow Datasets A large synthetic dataset for machine learning applications in power transmission grids,

Reference 2

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Observation 0d0d200a-dcbc-4bd1-a54c-d7ce6f46f8b1 · outbound

This paper cites Efficient creation of datasets for data-driven power system applications,.

Constrained Diffusion Models for Synthesizing Representative Power Flow Datasets Efficient creation of datasets for data-driven power system applications,

Reference 3

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T04:19:17.431301Z

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Observation a2be7d8c-146a-4836-b7c9-54821a3538c3 · outbound

This paper cites Machine learning for optimal power flows,.

Constrained Diffusion Models for Synthesizing Representative Power Flow Datasets Machine learning for optimal power flows,

Reference 4

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Observation e9c9c3fa-c47a-47f8-a77a-43d738ff0a4c · outbound

This paper cites Embedding Power Flow into Machine Learning for Parameter and State Estimation.

Constrained Diffusion Models for Synthesizing Representative Power Flow Datasets Embedding Power Flow into Machine Learning for Parameter and State Estimation

Reference 5

Resolution: unresolved; no resolver link, observed 2026-08-07T04:19:01.076411Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 239d78c7-a137-4940-a231-0a069544dc8b · outbound

This paper cites OPF-Learn: An open-source framework for creating representative AC optimal power flow datasets,.

Constrained Diffusion Models for Synthesizing Representative Power Flow Datasets OPF-Learn: An open-source framework for creating representative AC optimal power flow datasets,

Reference 6

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T04:19:16.638410Z

Source-reported events for the cited work

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Observation 91ea05d9-6eed-4131-bce7-7b5f827e823b · outbound

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

Constrained Diffusion Models for Synthesizing Representative Power Flow Datasets Generating quality datasets for real-time security assessment: Balancing historically relevant and rare feasible operating conditions,

Reference 7

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Observation 684afde2-73ec-4ebc-b556-8243a7da4691 · outbound

This paper cites Privacy-preserving and approximately truthful local electricity markets: A differentially private VCG mechanism,.

Constrained Diffusion Models for Synthesizing Representative Power Flow Datasets Privacy-preserving and approximately truthful local electricity markets: A differentially private VCG mechanism,

Reference 8

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T04:19:16.155088Z

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Observation 7da593d6-b93b-4bce-9fed-678515402a77 · outbound

This paper cites Differentially private algorithms for synthetic power system datasets,.

Constrained Diffusion Models for Synthesizing Representative Power Flow Datasets Differentially private algorithms for synthetic power system datasets,

Reference 9

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T04:19:15.879983Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation de626aff-34c2-48fa-95ab-b6980410bab0 · outbound

This paper cites Synthesizing grid data with cyber resilience and privacy guarantees,.

Constrained Diffusion Models for Synthesizing Representative Power Flow Datasets Synthesizing grid data with cyber resilience and privacy guarantees,

Reference 10

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T04:19:15.589363Z

Source-reported events for the cited work

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Observation bd691cf9-bb96-4d6f-be68-60c98d809312 · outbound

This paper cites Generating Synthetic Net Load Data with Physics-informed Diffusion Model.

Constrained Diffusion Models for Synthesizing Representative Power Flow Datasets Generating Synthetic Net Load Data with Physics-informed Diffusion Model

Reference 11

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Observation 264828f8-56e1-4f7a-b7f9-c80e3d4075b0 · outbound

This paper cites Generating multivariate load states using a conditional variational autoencoder,.

Constrained Diffusion Models for Synthesizing Representative Power Flow Datasets Generating multivariate load states using a conditional variational autoencoder,

Reference 12

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T04:19:15.319807Z

Source-reported events for the cited work

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Observation 7aa3b0b5-9907-4b7a-948f-6d2b18c7d2a1 · outbound

This paper cites Data-driven EV load profiles generation using a variational auto-encoder,.

Constrained Diffusion Models for Synthesizing Representative Power Flow Datasets Data-driven EV load profiles generation using a variational auto-encoder,

Reference 13

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T04:19:15.009986Z

Source-reported events for the cited work

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Observation f40ce17c-15b4-45cf-9289-4a76f70ce8fd · outbound

This paper cites Generating contextual load profiles using a conditional variational autoencoder,.

Constrained Diffusion Models for Synthesizing Representative Power Flow Datasets Generating contextual load profiles using a conditional variational autoencoder,

Reference 14

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Source-reported events for the cited work

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Observation 7a85732b-65d3-4038-b969-8c371b8a5b6d · outbound

This paper cites Autoencoding beyond pixels using a learned similarity metric,.

Constrained Diffusion Models for Synthesizing Representative Power Flow Datasets Autoencoding beyond pixels using a learned similarity metric,

Reference 15

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Source-reported events for the cited work

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Observation 48ac03f3-4929-466c-af5f-aad83e740af4 · outbound

This paper cites Generative adversarial network for synthetic time series data generation in smart grids,.

Constrained Diffusion Models for Synthesizing Representative Power Flow Datasets Generative adversarial network for synthetic time series data generation in smart grids,

Reference 16

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T04:19:14.070522Z

Source-reported events for the cited work

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Observation 9181f38d-13ef-451c-8f6d-8db0eb4b5500 · outbound

This paper cites GAN-based model for residential load generation consid- ering typical consumption patterns,.

Constrained Diffusion Models for Synthesizing Representative Power Flow Datasets GAN-based model for residential load generation consid- ering typical consumption patterns,

Reference 17

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Source-reported events for the cited work

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Observation 073fd625-1124-46e7-bdf2-e385638fc48f · outbound

This paper cites Generating energy data for machine learning with recurrent generative adversarial networks,.

Constrained Diffusion Models for Synthesizing Representative Power Flow Datasets Generating energy data for machine learning with recurrent generative adversarial networks,

Reference 18

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Source-reported events for the cited work

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Observation 3a49c8d2-221a-4d28-b714-e543a86dae11 · outbound

This paper cites A data-driven approach for generating synthetic load patterns and usage habits,.

Constrained Diffusion Models for Synthesizing Representative Power Flow Datasets A data-driven approach for generating synthetic load patterns and usage habits,

Reference 19

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Observation d1d674f4-62c7-474e-a559-4b3e75bf9741 · outbound

This paper cites Model-free renewable scenario generation using gener- ative adversarial networks,.

Constrained Diffusion Models for Synthesizing Representative Power Flow Datasets Model-free renewable scenario generation using gener- ative adversarial networks,

Reference 20

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T04:19:12.615649Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation ba75955f-0684-4d3c-ba68-96300098a368 · outbound

This paper cites Constrained Diffusion Models for Synthesizing Representative Power Flow Datasets.

Constrained Diffusion Models for Synthesizing Representative Power Flow Datasets Constrained Diffusion Models for Synthesizing Representative Power Flow Datasets

Reference 21

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Observation ed9aed29-e86a-44b8-a129-cbcae77bd5b8 · outbound

This paper cites Learning to solve the AC-OPF using sensitivity- informed deep neural networks,.

Constrained Diffusion Models for Synthesizing Representative Power Flow Datasets Learning to solve the AC-OPF using sensitivity- informed deep neural networks,

Reference 22

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T04:19:12.311721Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation 33760962-7df7-41f4-b725-c25e0c6db2d0 · outbound

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

Constrained Diffusion Models for Synthesizing Representative Power Flow Datasets Predicting AC optimal power flows: Combining deep learning and lagrangian dual methods,

Reference 23

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T04:19:11.910460Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation 38431929-dd75-403d-b0a6-9f8d69ed260a · outbound

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

Constrained Diffusion Models for Synthesizing Representative Power Flow Datasets Learning optimal solutions for extremely fast AC optimal power flow,

Reference 24

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T04:19:11.582704Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation 2efb1506-e472-47a4-bf6c-31482028207b · outbound

This paper cites Scalable bilevel optimization for gener- ating maximally representative OPF datasets,.

Constrained Diffusion Models for Synthesizing Representative Power Flow Datasets Scalable bilevel optimization for gener- ating maximally representative OPF datasets,

Reference 25

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T04:19:11.201514Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation 4ad602c9-f3b2-40b8-ad75-a2da92d55c59 · outbound

This paper cites On the robustness of machine-learnt proxies for security constrained optimal power flow solvers,.

Constrained Diffusion Models for Synthesizing Representative Power Flow Datasets On the robustness of machine-learnt proxies for security constrained optimal power flow solvers,

Reference 26

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T04:19:10.943807Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation 3a02f3a7-15de-43e5-a9fd-11c706c41030 · outbound

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

Constrained Diffusion Models for Synthesizing Representative Power Flow Datasets OPFData: Large-scale datasets for AC optimal power flow with topological perturbations

Reference 27

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Source-reported events for the cited work

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Observation d9d06b2b-60d8-40d3-8c4c-a528a3450864 · outbound

This paper cites Decision Theoretic Foundations for Conformal Prediction: Optimal Uncertainty Quantification for Risk-Averse Agents.

Constrained Diffusion Models for Synthesizing Representative Power Flow Datasets Decision Theoretic Foundations for Conformal Prediction: Optimal Uncertainty Quantification for Risk-Averse Agents

Reference 28

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Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation c24cae0e-302c-4445-ad70-eb29c23f2ca5 · outbound

This paper cites A tutorial on conformal prediction.

Constrained Diffusion Models for Synthesizing Representative Power Flow Datasets A tutorial on conformal prediction

Reference 29

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T04:19:10.757614Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation 3cf23008-9d20-42df-875a-e4fb5f41e5fc · outbound

This paper cites Representation learning: A review and new perspec- tives,.

Constrained Diffusion Models for Synthesizing Representative Power Flow Datasets Representation learning: A review and new perspec- tives,

Reference 30

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T04:19:10.584641Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation fcab913a-c161-4e3a-9f98-012c34976b53 · outbound

This paper cites Closing the Loop: A Framework for Trustworthy Machine Learning in Power Systems.

Constrained Diffusion Models for Synthesizing Representative Power Flow Datasets Closing the Loop: A Framework for Trustworthy Machine Learning in Power Systems

Reference 31

Resolution: verified exact; local_arxiv, observed 2026-08-07T04:19:07.210498Z

Source-reported events for the cited work

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Observation b1eebaf6-3980-44f0-b676-391db52b7028 · outbound

This paper cites A survey on generative adversarial networks: Variants, applications, and training,.

Constrained Diffusion Models for Synthesizing Representative Power Flow Datasets A survey on generative adversarial networks: Variants, applications, and training,

Reference 32

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T04:19:10.412632Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation 75862127-90e1-4eba-a9d2-4ac228ee7dd5 · outbound

This paper cites Diffusion models beat GANs on image synthesis,.

Constrained Diffusion Models for Synthesizing Representative Power Flow Datasets Diffusion models beat GANs on image synthesis,

Reference 33

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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation 4905dc11-2260-4814-bb2f-fbd158843388 · outbound

This paper cites Short-term wind power scenario generation based on conditional latent diffusion models,.

Constrained Diffusion Models for Synthesizing Representative Power Flow Datasets Short-term wind power scenario generation based on conditional latent diffusion models,

Reference 34

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T04:19:10.065793Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation e37d63cf-fc55-4443-86c2-eef952578527 · outbound

This paper cites Diffcharge: Generating EV charging scenarios via a denoising diffusion model,.

Constrained Diffusion Models for Synthesizing Representative Power Flow Datasets Diffcharge: Generating EV charging scenarios via a denoising diffusion model,

Reference 35

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T04:19:09.912813Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation 738acfb7-e9f7-4af2-b617-3bbd2a63f2b3 · outbound

This paper cites Denoising diffusion probabilistic models,.

Constrained Diffusion Models for Synthesizing Representative Power Flow Datasets Denoising diffusion probabilistic models,

Reference 36

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Source-reported events for the cited work

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Observation 6591188c-37f8-43e0-b2d2-d2de9752956b · outbound

This paper cites A survey of relaxations and approximations of the power flow equations,.

Constrained Diffusion Models for Synthesizing Representative Power Flow Datasets A survey of relaxations and approximations of the power flow equations,

Reference 37

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T04:19:09.528479Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation ee0f6af8-ccea-417f-ad85-3005489dae73 · outbound

This paper cites Neural Approximate Mirror Maps for Constrained Diffusion Models.

Constrained Diffusion Models for Synthesizing Representative Power Flow Datasets Neural Approximate Mirror Maps for Constrained Diffusion Models

Reference 38

Resolution: unresolved; no resolver link, observed 2026-08-07T04:19:05.672485Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 6f836865-0020-47c9-a508-e5568108c853 · outbound

This paper cites Consistent diffusion models: Mitigating sampling drift by learning to be consistent,.

Constrained Diffusion Models for Synthesizing Representative Power Flow Datasets Consistent diffusion models: Mitigating sampling drift by learning to be consistent,

Reference 39

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T04:19:09.354213Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation c79205d0-b503-4b7e-a138-bcbec2edba02 · outbound

This paper cites Improving diffusion models for inverse problems using manifold constraints,.

Constrained Diffusion Models for Synthesizing Representative Power Flow Datasets Improving diffusion models for inverse problems using manifold constraints,

Reference 40

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T04:19:09.147631Z

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No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation 4cf2854f-1326-43b7-aca3-80040d099c5b · outbound

This paper cites Boumal, An introduction to optimization on smooth manifolds.

Constrained Diffusion Models for Synthesizing Representative Power Flow Datasets Boumal, An introduction to optimization on smooth manifolds

Reference 41

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T04:19:08.864449Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation da9586d8-bd11-4811-8893-130d8a7d19c4 · outbound

This paper cites Supplemental materials,.

Constrained Diffusion Models for Synthesizing Representative Power Flow Datasets Supplemental materials,

Reference 42

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T04:19:08.557293Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation 35fc48a4-2e3e-4278-913d-a0e40ae396ad · outbound

This paper cites Fast-decoupled power flow method for inte- grated analysis of transmission and distribution systems,.

Constrained Diffusion Models for Synthesizing Representative Power Flow Datasets Fast-decoupled power flow method for inte- grated analysis of transmission and distribution systems,

Reference 43

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T04:19:08.307067Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation 6d999c55-d461-4e18-96f4-1cc03e09a358 · outbound

This paper cites Monticelli, State estimation in electric power systems: a generalized approach.

Constrained Diffusion Models for Synthesizing Representative Power Flow Datasets Monticelli, State estimation in electric power systems: a generalized approach

Reference 44

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T04:19:08.021770Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

Observation f00a5db0-d4f9-4c50-ac2f-d7467ea87821 · outbound

This paper cites an unresolved cited work.

Constrained Diffusion Models for Synthesizing Representative Power Flow Datasets Unresolved cited work

Reference 45

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Source-reported events for the cited work

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Observation abd8d30c-d7f0-4936-a6b5-ff1a1391780d · outbound

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

Constrained Diffusion Models for Synthesizing Representative Power Flow Datasets The Power Grid Library for Benchmarking AC Optimal Power Flow Algorithms

Reference 46

Resolution: unresolved; no resolver link, observed 2026-08-07T04:19:06.781034Z

Source-reported events for the cited work

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Observation cc88517e-4e76-4e46-9469-8bdcf18383e9 · outbound

This paper cites Computational optimal transport,.

Constrained Diffusion Models for Synthesizing Representative Power Flow Datasets Computational optimal transport,

Reference 47

Resolution: unresolved; no resolver link, observed 2026-08-07T04:19:06.946738Z

Source-reported events for the cited work

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Pith citing papers

Observation ba75955f-0684-4d3c-ba68-96300098a368 · inbound