Closing the Loop: A Framework for Trustworthy Machine Learning in Power Systems

14 March 2022

Papers citing "Closing the Loop: A Framework for Trustworthy Machine Learning in Power Systems"

7 / 7 papers shown

Title
Enriching Neural Network Training Dataset to Improve Worst-Case Performance Guarantees Rahul Nellikkath Spyros Chatzivasileiadis 36 3 0 23 Mar 2023
Physics Informed Neural Networks for Phase Locked Loop Transient Stability Assessment Rahul Nellikkath Andreas Venzke M. Bakhshizadeh Ilgiz Murzakhanov Spyros Chatzivasileiadis 18 4 0 21 Mar 2023
Physics-Informed Neural Networks for Time-Domain Simulations: Accuracy, Computational Cost, and Flexibility Jochen Stiasny Spyros Chatzivasileiadis PINN AI4CE 27 9 0 15 Mar 2023
Minimizing Worst-Case Violations of Neural Networks Rahul Nellikkath Spyros Chatzivasileiadis 38 3 0 21 Dec 2022
Modeling the AC Power Flow Equations with Optimally Compact Neural Networks: Application to Unit Commitment Alyssa Kody Samuel C. Chevalier Spyros Chatzivasileiadis Daniel Molzahn 64 37 0 21 Oct 2021
Physics-Informed Graphical Neural Network for Parameter & State Estimations in Power Systems Laurent Pagnier Michael Chertkov 34 49 0 12 Feb 2021
Input Convex Neural Networks Brandon Amos Lei Xu J. Zico Kolter 178 598 0 22 Sep 2016