arXiv Machine Learning July 13, 2026 By Ferran Bohigas-Daranas, Hamid Latif-Martinez, Eduardo Prieto-Araujo, Pere Barlet-Ros, Oriol Gomis-Bellmunt

Power Flow Feasibility Assessment Using Variational Graph Autoencoders

Read the original on arXiv Machine Learning →

arXiv:2607. 09122v1 Announce Type: new Abstract: Data-driven methods, including graph neural networks, have been studied for accelerating power flow calculations in recent years, but very little attention has been paid to the solution feasibility, which can be obtained by traditional solvers.

Summary generated by The Flow from the publisher's feed. The full article lives at arXiv Machine Learning.

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