Papers
Communities
Events
Blog
Pricing
Search
Open menu
Home
Papers
2006.16356
Cited By
High-Fidelity Machine Learning Approximations of Large-Scale Optimal Power Flow
29 June 2020
Minas Chatzos
Ferdinando Fioretto
Terrence W.K. Mak
Pascal Van Hentenryck
AI4CE
Re-assign community
ArXiv (abs)
PDF
HTML
Papers citing
"High-Fidelity Machine Learning Approximations of Large-Scale Optimal Power Flow"
10 / 10 papers shown
Title
Towards graph neural networks for provably solving convex optimization problems
Chendi Qian
Christopher Morris
96
1
0
04 Feb 2025
PowerGraph: A power grid benchmark dataset for graph neural networks
Anna Varbella
Kenza Amara
B. Gjorgiev
Mennatallah El-Assady
G. Sansavini
46
9
0
05 Feb 2024
Unsupervised Deep Learning for AC Optimal Power Flow via Lagrangian Duality
Ke Chen
Shourya Bose
Yu Zhang
52
7
0
07 Dec 2022
Confidence-Aware Graph Neural Networks for Learning Reliability Assessment Commitments
Seonho Park
Wenbo Chen
Dahyeon Han
Mathieu Tanneau
Pascal Van Hentenryck
99
28
0
28 Nov 2022
The intersection of machine learning with forecasting and optimisation: theory and applications
M. Abolghasemi
67
2
0
24 Nov 2022
Self-Supervised Primal-Dual Learning for Constrained Optimization
Seonho Park
Pascal Van Hentenryck
81
51
0
18 Aug 2022
Ensuring DNN Solution Feasibility for Optimization Problems with Convex Constraints and Its Application to DC Optimal Power Flow Problems
Tianyu Zhao
Xiang Pan
Minghua Chen
S. Low
86
10
0
15 Dec 2021
OPF-Learn: An Open-Source Framework for Creating Representative AC Optimal Power Flow Datasets
Trager Joswig-Jones
K. Baker
Ahmed S. Zamzam
52
29
0
01 Nov 2021
Enforcing Policy Feasibility Constraints through Differentiable Projection for Energy Optimization
Bingqing Chen
Neural Network
Kyri Baker
J. Zico Kolter
Mario Berges
88
61
0
19 May 2021
Learning-Accelerated ADMM for Distributed Optimal Power Flow
David J. Biagioni
P. Graf
Xinming Zhang
Ahmed S. Zamzam
K. Baker
J. King
59
8
0
08 Nov 2019
1