Typed states for the displayed outbound observations.
Source: paper_references, paper_reference_links, observed 2026-08-15T20:41:18.030398Z
Paper Citation Record · LEDGER
SenseFlow: A Physics-Informed and Self-Ensembling Iterative Framework for Power Flow Estimation
As of 18 August 2026, this Paper Citation Record lists 33 of 33 outbound references and 0 inbound Pith citation observations for arXiv:2505.12302.
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 2026-08-18T06:34:40.430872+00:00
Pith citing papers itemized under the disclosed page cap.
Source: paper_references, paper_reference_links
A source-named dated measurement, never combined with another source.
Source: cited_works
Reference resolution
33 of 33 outbound references displayed
- verified exact0
- verified fuzzy29
- unresolved4
- parse uncertain0
- malformed identifier0
- metadata mismatch0
External citation measurements
No source-named external measurement is stored.
Outbound references
Observation c9217161-622a-4511-bb20-0fd598f8eb85 · outbound
This paper cites Artificial intelligence and machine learning for energy consumption and production in emerging markets: a review.
SenseFlow: A Physics-Informed and Self-Ensembling Iterative Framework for Power Flow Estimation Artificial intelligence and machine learning for energy consumption and production in emerging markets: a review
Reference 1
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-15T20:41:18.578609Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.
Observation 59a52e10-8b21-48ca-bffb-bb54078f184c · outbound
This paper cites Review of machine learning techniques for optimal power flow.
SenseFlow: A Physics-Informed and Self-Ensembling Iterative Framework for Power Flow Estimation Review of machine learning techniques for optimal power flow
Reference 2
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-15T20:41:18.562561Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.
Observation 18a7bdef-d339-44d2-9fd1-221bff5452e4 · outbound
This paper cites Physics- informed graphical neural network for power system state estimation.
SenseFlow: A Physics-Informed and Self-Ensembling Iterative Framework for Power Flow Estimation Physics- informed graphical neural network for power system state estimation
Reference 3
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-15T20:41:18.547544Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.
Observation ec0c02a6-06de-44c8-9091-8823f6613530 · outbound
This paper cites Developments in the newton raphson power flow formulation based on current injections.
SenseFlow: A Physics-Informed and Self-Ensembling Iterative Framework for Power Flow Estimation Developments in the newton raphson power flow formulation based on current injections
Reference 4
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-15T20:41:18.532513Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.
Observation 104ee1eb-478f-4ff1-a1d8-e73bbef3791e · outbound
This paper cites Load flow analysis by gauss-seidel method; a survey.
SenseFlow: A Physics-Informed and Self-Ensembling Iterative Framework for Power Flow Estimation Load flow analysis by gauss-seidel method; a survey
Reference 5
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-15T20:41:18.516848Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.
Observation 480f4120-4418-44b6-bb15-97fc8ee9b30a · outbound
This paper cites Data-driven power flow calculation method: A lifting dimension linear regression approach.
SenseFlow: A Physics-Informed and Self-Ensembling Iterative Framework for Power Flow Estimation Data-driven power flow calculation method: A lifting dimension linear regression approach
Reference 6
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-15T20:41:18.499799Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.
Observation 0c91e066-07d5-49d2-bc0f-1871876c8e0d · outbound
This paper cites Physics-guided deep neural networks for power flow analysis.
SenseFlow: A Physics-Informed and Self-Ensembling Iterative Framework for Power Flow Estimation Physics-guided deep neural networks for power flow analysis
Reference 7
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-15T20:41:18.483773Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.
Observation c8f66f0e-de5e-4432-807f-914eeea633c5 · outbound
This paper cites Machine learning and deep learning in energy systems: A review.
SenseFlow: A Physics-Informed and Self-Ensembling Iterative Framework for Power Flow Estimation Machine learning and deep learning in energy systems: A review
Reference 8
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-15T20:41:18.467925Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.
Observation fdd3cd3e-1916-494c-ac49-4cb18e1798f4 · outbound
This paper cites Semi-supervised classification with graph convolutional networks.
SenseFlow: A Physics-Informed and Self-Ensembling Iterative Framework for Power Flow Estimation Semi-supervised classification with graph convolutional networks
Reference 9
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-15T20:41:18.452241Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.
Observation e7ba29fe-fa38-4100-8e8c-c5b6ba8ad177 · outbound
This paper cites Powerflownet: Power flow approximation using message passing graph neural networks.
SenseFlow: A Physics-Informed and Self-Ensembling Iterative Framework for Power Flow Estimation Powerflownet: Power flow approximation using message passing graph neural networks
Reference 10
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-15T20:41:18.436738Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.
Observation 4c3d0218-dc8d-4abd-89cd-b7c07526f14b · outbound
This paper cites Physics-informed neural networks for ac optimal power flow.
SenseFlow: A Physics-Informed and Self-Ensembling Iterative Framework for Power Flow Estimation Physics-informed neural networks for ac optimal power flow
Reference 11
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-15T20:41:18.420551Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.
Observation c8b7163e-0308-4930-b145-5996d293cf04 · outbound
This paper cites Leveraging power grid topology in machine learning assisted optimal power flow.
SenseFlow: A Physics-Informed and Self-Ensembling Iterative Framework for Power Flow Estimation Leveraging power grid topology in machine learning assisted optimal power flow
Reference 12
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-15T20:41:18.403551Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.
Observation 806c29b1-54ca-42fb-acea-67224a2d2c27 · outbound
This paper cites An improved backward/forward sweep load flow algorithm for radial distribution systems.
SenseFlow: A Physics-Informed and Self-Ensembling Iterative Framework for Power Flow Estimation An improved backward/forward sweep load flow algorithm for radial distribution systems
Reference 13
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-15T20:41:18.386024Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.
Observation c38a1ba2-cae3-4831-b775-561d67334450 · outbound
This paper cites The holomorphic embedding load flow method.
SenseFlow: A Physics-Informed and Self-Ensembling Iterative Framework for Power Flow Estimation The holomorphic embedding load flow method
Reference 14
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-15T20:41:18.367784Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.
Observation a189bf50-b319-49d3-b801-bf82ff54168d · outbound
This paper cites Power flow analysis.Computational Models in Engineering, pages 67–88, 2020.
SenseFlow: A Physics-Informed and Self-Ensembling Iterative Framework for Power Flow Estimation Power flow analysis.Computational Models in Engineering, pages 67–88, 2020
Reference 15
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-15T20:41:18.350152Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.
Observation 57b467a6-8c15-42e2-a6e3-33ad0b922a5f · outbound
This paper cites MIT Press, 2016.
SenseFlow: A Physics-Informed and Self-Ensembling Iterative Framework for Power Flow Estimation MIT Press, 2016
Reference 16
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-15T20:41:18.334208Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.
Observation d5fbbb2c-bfe8-4ade-9727-ce8e122df5ac · outbound
This paper cites Data-driven piecewise linearization for distribu- tion three-phase stochastic power flow.
SenseFlow: A Physics-Informed and Self-Ensembling Iterative Framework for Power Flow Estimation Data-driven piecewise linearization for distribu- tion three-phase stochastic power flow
Reference 17
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-15T20:41:18.318740Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.
Observation 302f4704-efe6-4041-94dc-05086841e686 · outbound
This paper cites A comprehensive survey on graph neural networks.
SenseFlow: A Physics-Informed and Self-Ensembling Iterative Framework for Power Flow Estimation A comprehensive survey on graph neural networks
Reference 18
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-15T20:41:18.302945Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.
Observation 03b08b26-f8f3-4a33-b05a-f7da1d9f3d08 · outbound
This paper cites Deep learning on graphs: A survey.IEEE Transactions on Knowledge and Data Engineering, 34(1):249–270, 2020.
SenseFlow: A Physics-Informed and Self-Ensembling Iterative Framework for Power Flow Estimation Deep learning on graphs: A survey.IEEE Transactions on Knowledge and Data Engineering, 34(1):249–270, 2020
Reference 19
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-15T20:41:18.286770Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.
Observation 799833da-7ba0-4828-9dad-33208cfa803e · outbound
This paper cites A review of graph neural networks and their applications in power systems.
SenseFlow: A Physics-Informed and Self-Ensembling Iterative Framework for Power Flow Estimation A review of graph neural networks and their applications in power systems
Reference 20
- Resolution
- unresolved
- no resolver link, observed 2026-08-15T20:41:17.963777Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation 612e9813-1f3b-42f4-a6c4-13efbc4e01dc · outbound
This paper cites Power flow analysis via typed graph neural networks.
SenseFlow: A Physics-Informed and Self-Ensembling Iterative Framework for Power Flow Estimation Power flow analysis via typed graph neural networks
Reference 21
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-15T20:41:18.256740Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.
Observation abab3f75-6493-4180-82fc-4535ee562008 · outbound
This paper cites Optimal power flow using graph neural networks.
SenseFlow: A Physics-Informed and Self-Ensembling Iterative Framework for Power Flow Estimation Optimal power flow using graph neural networks
Reference 22
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-15T20:41:18.240480Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.
Observation fd418e5e-aeb6-46f7-a5e8-7961d42f20b9 · outbound
This paper cites Physics embedded graph convolution neural network for power flow calculation considering uncertain injections and topology.
SenseFlow: A Physics-Informed and Self-Ensembling Iterative Framework for Power Flow Estimation Physics embedded graph convolution neural network for power flow calculation considering uncertain injections and topology
Reference 23
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-15T20:41:18.224035Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.
Observation d7c640e3-d8db-423b-8aab-fea6b2a00315 · outbound
This paper cites Physics-informed geometric deep learning for inference tasks in power systems.
SenseFlow: A Physics-Informed and Self-Ensembling Iterative Framework for Power Flow Estimation Physics-informed geometric deep learning for inference tasks in power systems
Reference 24
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-15T20:41:18.207195Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.
Observation f3ba3a42-73ea-473c-8561-3fd9e15a1e47 · outbound
This paper cites Deep statistical solver for distribution system state estimation.
SenseFlow: A Physics-Informed and Self-Ensembling Iterative Framework for Power Flow Estimation Deep statistical solver for distribution system state estimation
Reference 25
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-15T20:41:18.190758Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.
Observation 5aea1bd9-58e5-43d4-81d8-aea703581829 · outbound
This paper cites Weisfeiler and leman go neural: Higher-order graph neural networks.
SenseFlow: A Physics-Informed and Self-Ensembling Iterative Framework for Power Flow Estimation Weisfeiler and leman go neural: Higher-order graph neural networks
Reference 26
- Resolution
- unresolved
- no resolver link, observed 2026-08-15T20:41:17.993598Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation 479aa8d3-74ae-4250-83dd-c3da4ef13e70 · outbound
This paper cites Graph attention networks.
SenseFlow: A Physics-Informed and Self-Ensembling Iterative Framework for Power Flow Estimation Graph attention networks
Reference 27
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-15T20:41:18.164243Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.
Observation 2fd3aabd-43de-4580-8aba-fa65f97d9c9d · outbound
This paper cites Mat- power: Steady-state operations, planning, and analysis tools for power systems research and education.
SenseFlow: A Physics-Informed and Self-Ensembling Iterative Framework for Power Flow Estimation Mat- power: Steady-state operations, planning, and analysis tools for power systems research and education
Reference 28
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-15T20:41:18.148420Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.
Observation 47e5715f-f1f8-4342-8036-69c83309f89d · outbound
This paper cites Strategies for pre-training graph neural networks.
SenseFlow: A Physics-Informed and Self-Ensembling Iterative Framework for Power Flow Estimation Strategies for pre-training graph neural networks
Reference 29
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-15T20:41:18.131871Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.
Observation 6897fd6a-1a6e-4ea4-84a5-6e39e030bf3d · outbound
This paper cites Inductive representation learning on large graphs.
SenseFlow: A Physics-Informed and Self-Ensembling Iterative Framework for Power Flow Estimation Inductive representation learning on large graphs
Reference 30
- Resolution
- unresolved
- no resolver link, observed 2026-08-15T20:41:18.013966Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation f68bae8c-da71-4e9d-9da4-e9dee780334b · outbound
This paper cites Residual Gated Graph ConvNets.
SenseFlow: A Physics-Informed and Self-Ensembling Iterative Framework for Power Flow Estimation Residual Gated Graph ConvNets
Reference 31
- Resolution
- unresolved
- no resolver link, observed 2026-08-15T20:41:18.019895Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation c21d078b-2964-4564-b646-2ef06cfd7d48 · outbound
This paper cites How attentive are graph attention networks? In International Conference on Learning Representations (ICLR), 2022.
SenseFlow: A Physics-Informed and Self-Ensembling Iterative Framework for Power Flow Estimation How attentive are graph attention networks? In International Conference on Learning Representations (ICLR), 2022
Reference 32
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-15T20:41:18.104854Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.
Observation d1743642-bc81-4767-add4-422a458eb21f · outbound
This paper cites Masked label prediction: Unified message passing model for semi-supervised classification.
SenseFlow: A Physics-Informed and Self-Ensembling Iterative Framework for Power Flow Estimation Masked label prediction: Unified message passing model for semi-supervised classification
Reference 33
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-15T20:41:18.087487Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.
Pith citing papers
No inbound Pith citation observations are available.