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PowerFlowNet: Power Flow Approximation Using Message Passing Graph Neural Networks
6 November 2023
Nan Lin
Stavros Orfanoudakis
Nathan Ordonez Cardenas
Juan S. Giraldo
Pedro P. Vergara
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Papers citing
"PowerFlowNet: Power Flow Approximation Using Message Passing Graph Neural Networks"
6 / 6 papers shown
Test Time Training for AC Power Flow Surrogates via Physics and Operational Constraint Refinement
P. Dogoulis
Mohammad Iman Alizadeh
Sylvain Kubler
Maxime Cordy
36
0
0
27 Nov 2025
PF
Δ
Δ
Δ
: A Benchmark Dataset for Power Flow under Load, Generation, and Topology Variations
Ana K. Rivera
Anvita Bhagavathula
Alvaro Carbonero
Priya Donti
168
0
0
24 Oct 2025
Physics-Informed Inductive Biases for Voltage Prediction in Distribution Grids
Ehimare Okoyomon
Arbel Yaniv
Christoph Goebel
AI4CE
138
2
0
29 Sep 2025
Physics-informed GNN for medium-high voltage AC power flow with edge-aware attention and line search correction operator
Changhun Kim
Timon Conrad
R. Karim
Julian Oelhaf
David Riebesel
T. Arias-Vergara
Andreas Maier
Johann Jager
Siming Bayer
162
1
0
26 Sep 2025
Study Design and Demystification of Physics Informed Neural Networks for Power Flow Simulation
Milad Leyli-abadi
Antoine Marot
Jérôme Picault
AI4CE
125
0
0
23 Sep 2025
SenseFlow: A Physics-Informed and Self-Ensembling Iterative Framework for Power Flow Estimation
Zhen Zhao
Wenqi Huang
Zicheng Wang
Jiaxuan Hou
Peng Li
Mengwei He
358
1
0
18 May 2025
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