4 papers
BOOST-RPF: Boosted Sequential Trees for Radial Power Flow
Ehimare Okoyomon, Christoph Goebel
Accurate power flow analysis is critical for modern distribution systems, yet classical solvers face scalability issues, and current machine learning models often struggle with gen…
Physics-Informed Inductive Biases for Voltage Prediction in Distribution Grids
Ehimare Okoyomon, Arbel Yaniv, Christoph Goebel
Voltage prediction in distribution grids is a critical yet difficult task for maintaining power system stability. Machine learning approaches, particularly Graph Neural Networks (G…
Exploring Variational Graph Autoencoders for Distribution Grid Data Generation
Syed Zain Abbas, Ehimare Okoyomon
To address the lack of public power system data for machine learning research in energy networks, we investigate the use of variational graph autoencoders (VGAEs) for synthetic dis…
On the Rademacher Complexity of Graph Neural Networks: Unifying Expressivity and Geometry
Martin Carrasco, Caio F. Deberaldini Netto, Vahan A. Martirosyan +3
Understanding the interplay between generalization, expressivity, and the geometry of the input space is a central challenge in graph learning. The expressivity of Graph Neural Net…