Adaptive Activation Functions for Deep Learning-based Power Flow Analysis. - Zeynab Kaseb; Yu Xiang; Peter Palensky; Pedro P. Vergara (ISGT EUROPE, 2023)

Quantum Neural Networks for Power Flow Analysis. - Zeynab Kaseb; Matthias Möller; Giorgio Tosti Balducci; Peter Palensky; Pedro P. Vergara (CoRR, 2023)

Hybrid Quantum Physics-Informed Neural Networks for Power Flow Analysis. - Zeynab Kaseb; Peter Palensky; Pedro P. Vergara (ISGT EUROPE, 2024)

Adiabatic Computing for Power Flow Analysis. - Zeynab Kaseb; Matthias Möller; Markus Kirsch; Peter Palensky; Pedro P. Vergara (QCE, 2024)

Adaptive Informed Deep Neural Networks for Power Flow Analysis. - Zeynab Kaseb; Stavros Orfanoudakis; Pedro P. Vergara; Peter Palensky (CoRR, 2024)

Solving Power System Problems using Adiabatic Quantum Computing. - Zeynab Kaseb; Matthias Möller; Peter Palensky; Pedro P. Vergara (CoRR, 2025)

Data driven approach towards more efficient Newton-Raphson power flow calculation for distribution grids. - Shengyuan Yan; Farzad Vazinram; Zeynab Kaseb; Lindsay Spoor; Jochen Stiasny; Betul Mamudi; Amirhossein Heydarian Ardakani; Ugochukwu Orji; Pedro P. Vergara; Yu Xiang; Jerry Guo (CoRR, 2025)

Quantum Hardware-in-the-Loop for Optimal Power Flow in Renewable-Integrated Power Systems. - Zeynab Kaseb; Rahul Rane; Aleksandra Lekic; Matthias Möller; Amin Khodaei; Peter Palensky; Pedro P. Vergara (CoRR, 2025)

A Quantum-Enhanced Power Flow and Optimal Power Flow based on Combinatorial Reformulation. - Zeynab Kaseb; Matthias Möller; Peter Palensky; Pedro P. Vergara (CoRR, 2025)