Paper Citation Record · LEDGER

Synthetic Power Flow Data Generation Using Physics-Informed Denoising Diffusion Probabilistic Models

As of 16 August 2026, this Paper Citation Record lists 24 of 24 outbound references and 0 inbound Pith citation observations for arXiv:2504.17210.

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

pith.paper-citation-record.v1
2504.17210 v1

Coverage vector

measured 24 of 24 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-16T10:50:52.199253Z

measured 24 of 24 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-16T06:30:59.297886+00:00

measured 0 of 0 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links

measured 0 of 1 external citation measurements

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

Source: cited_works

Reference resolution

24 of 24 outbound references displayed

verified exact0
verified fuzzy24
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malformed identifier0
metadata mismatch0

External citation measurements

No source-named external measurement is stored.

Outbound references

Observation 1ed20afe-f8c1-43f0-bb68-2b62cb2a8741 · outbound

This paper cites Smart grid standards: specifications, requirements, and technologies.

Synthetic Power Flow Data Generation Using Physics-Informed Denoising Diffusion Probabilistic Models Smart grid standards: specifications, requirements, and technologies

Reference 1

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T10:50:52.575859Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation 22104c21-77a3-4886-b908-4b5e64738649 · outbound

This paper cites Fast optimal power flow with guarantees via an unsupervised generative model.

Synthetic Power Flow Data Generation Using Physics-Informed Denoising Diffusion Probabilistic Models Fast optimal power flow with guarantees via an unsupervised generative model

Reference 2

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T10:50:52.560868Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation eced4298-f061-4ac5-b714-22c41f2f74dd · outbound

This paper cites Data-driven power flow linearization: A regression ap- proach.

Synthetic Power Flow Data Generation Using Physics-Informed Denoising Diffusion Probabilistic Models Data-driven power flow linearization: A regression ap- proach

Reference 3

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T10:50:52.546012Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation 31184fd8-2f3e-42d0-b900-e28fdaa6076f · outbound

This paper cites An introduction to variational autoen- coders.

Synthetic Power Flow Data Generation Using Physics-Informed Denoising Diffusion Probabilistic Models An introduction to variational autoen- coders

Reference 4

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T10:50:52.531208Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation bc9032b5-495c-4a5f-b174-8b2978efd993 · outbound

This paper cites Generative adversarial nets.

Synthetic Power Flow Data Generation Using Physics-Informed Denoising Diffusion Probabilistic Models Generative adversarial nets

Reference 5

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T10:50:52.516758Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation 543f1bd1-821e-4fff-9daf-ef3ca7f6e804 · outbound

This paper cites Denoising diffusion probabilistic mod- els.

Synthetic Power Flow Data Generation Using Physics-Informed Denoising Diffusion Probabilistic Models Denoising diffusion probabilistic mod- els

Reference 6

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T10:50:52.502500Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation 0fd30125-5d08-4d02-9140-cabbae0ffed4 · outbound

This paper cites Melgan: Generative adversarial networks for conditional waveform synthesis.

Synthetic Power Flow Data Generation Using Physics-Informed Denoising Diffusion Probabilistic Models Melgan: Generative adversarial networks for conditional waveform synthesis

Reference 7

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T10:50:52.488375Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation 5fdee0a0-9517-468f-b26c-b236fe640589 · outbound

This paper cites Video diffusion models.

Synthetic Power Flow Data Generation Using Physics-Informed Denoising Diffusion Probabilistic Models Video diffusion models

Reference 8

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T10:50:52.473793Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation 027f7922-7b55-49ad-b38a-81d58ad9b8d2 · outbound

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

Synthetic Power Flow Data Generation Using Physics-Informed Denoising Diffusion Probabilistic Models A data-driven approach for generating synthetic load patterns and usage habits

Reference 9

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T10:50:52.458843Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation 0eeccb7e-74e7-4863-aac2-fd619ba68227 · outbound

This paper cites Stochastic variational inference for probabilistic op- timal power flows.

Synthetic Power Flow Data Generation Using Physics-Informed Denoising Diffusion Probabilistic Models Stochastic variational inference for probabilistic op- timal power flows

Reference 10

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T10:50:52.444325Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation f71677a9-9674-49b1-a02f-6dd2e011b151 · outbound

This paper cites Generating multivariate load states using a condi- tional variational autoencoder.

Synthetic Power Flow Data Generation Using Physics-Informed Denoising Diffusion Probabilistic Models Generating multivariate load states using a condi- tional variational autoencoder

Reference 11

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T10:50:52.429255Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation 0b4ae58d-b5a6-4e60-b791-f170120b6df0 · outbound

This paper cites Anomaly detection using lstm- based variational autoencoder in unsupervised data in power grid.

Synthetic Power Flow Data Generation Using Physics-Informed Denoising Diffusion Probabilistic Models Anomaly detection using lstm- based variational autoencoder in unsupervised data in power grid

Reference 12

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T10:50:52.415020Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation 95c61b55-9901-4608-a9ca-ddf45c3e71a4 · outbound

This paper cites Power system state estimation using conditional genera- tive adversarial network.

Synthetic Power Flow Data Generation Using Physics-Informed Denoising Diffusion Probabilistic Models Power system state estimation using conditional genera- tive adversarial network

Reference 13

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T10:50:52.400993Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation b92c47ff-9d67-4f9f-8a5f-1d15a337251d · outbound

This paper cites A fully data-driven method based on generative adversarial networks for power system dynamic security assessment with missing data.

Synthetic Power Flow Data Generation Using Physics-Informed Denoising Diffusion Probabilistic Models A fully data-driven method based on generative adversarial networks for power system dynamic security assessment with missing data

Reference 14

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T10:50:52.387042Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation 096e7a5e-df2b-4f4d-8e45-96e2340d5b68 · outbound

This paper cites Synthetic time-series load data via conditional generative adversarial networks.

Synthetic Power Flow Data Generation Using Physics-Informed Denoising Diffusion Probabilistic Models Synthetic time-series load data via conditional generative adversarial networks

Reference 15

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T10:50:52.372731Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation 75e05f1f-ecda-411b-b98d-a4f53f06c8c1 · outbound

This paper cites Power system analysis.

Synthetic Power Flow Data Generation Using Physics-Informed Denoising Diffusion Probabilistic Models Power system analysis

Reference 16

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T10:50:52.358463Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation 95bb8800-f32b-4d8b-95bf-cc80fba1987e · outbound

This paper cites Deep unsupervised learning using nonequilib- rium thermodynamics.

Synthetic Power Flow Data Generation Using Physics-Informed Denoising Diffusion Probabilistic Models Deep unsupervised learning using nonequilib- rium thermodynamics

Reference 17

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T10:50:52.343578Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation f1e8cdf6-daa0-41d0-91cd-e7f1261e1c89 · outbound

This paper cites Deep learning.

Synthetic Power Flow Data Generation Using Physics-Informed Denoising Diffusion Probabilistic Models Deep learning

Reference 18

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T10:50:52.328600Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation 2f03c87e-deac-438f-b96e-4a2f225abf47 · outbound

This paper cites ”Understanding diffusion models: A unified perspective.”, 2022.

Synthetic Power Flow Data Generation Using Physics-Informed Denoising Diffusion Probabilistic Models ”Understanding diffusion models: A unified perspective.”, 2022

Reference 19

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T10:50:52.313200Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation 379caf44-714b-40ba-9d3f-7ca2a0a791ab · outbound

This paper cites MATPOWER: Steady-state operations, planning, and analysis tools for power systems research and education.

Synthetic Power Flow Data Generation Using Physics-Informed Denoising Diffusion Probabilistic Models MATPOWER: Steady-state operations, planning, and analysis tools for power systems research and education

Reference 20

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T10:50:52.297425Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation 85cde892-de07-443d-93cb-a9aab0feac65 · outbound

This paper cites Data-driven AC Optimal Power Flow with Physics-informed Learning and Calibrations.

Synthetic Power Flow Data Generation Using Physics-Informed Denoising Diffusion Probabilistic Models Data-driven AC Optimal Power Flow with Physics-informed Learning and Calibrations

Reference 21

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T10:50:52.282236Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation b68edb33-e9c6-4c1c-80c9-2a81a8f8b02d · outbound

This paper cites ”Topology-aware graph neural networks for learning feasible and adaptive AC-OPF solutions.” IEEE Transactions on Power Systems 38, no.

Synthetic Power Flow Data Generation Using Physics-Informed Denoising Diffusion Probabilistic Models ”Topology-aware graph neural networks for learning feasible and adaptive AC-OPF solutions.” IEEE Transactions on Power Systems 38, no

Reference 22

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T10:50:52.266739Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation b4d6c018-b64d-4c3d-b47f-1ae9a961986e · outbound

This paper cites ”Predicting ac optimal power flows: Combining deep learning and lagrangian dual methods.” In Proceedings of the AAAI conference on artificial intelligence, vol.

Synthetic Power Flow Data Generation Using Physics-Informed Denoising Diffusion Probabilistic Models ”Predicting ac optimal power flows: Combining deep learning and lagrangian dual methods.” In Proceedings of the AAAI conference on artificial intelligence, vol

Reference 23

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T10:50:52.251965Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Observation 08bdceb0-0755-4597-8212-d59d9edfaa82 · outbound

This paper cites Pixelcnn++: Improving the pixelcnn with discretized logistic mixture likelihood and other modifications.

Synthetic Power Flow Data Generation Using Physics-Informed Denoising Diffusion Probabilistic Models Pixelcnn++: Improving the pixelcnn with discretized logistic mixture likelihood and other modifications

Reference 24

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T10:50:52.236366Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.

Pith citing papers

No inbound Pith citation observations are available.