4 papers
Geometric Pareto Control: Physics-Supervised Pareto Representation Learning via Riemannian Energy-Gradient Flow
Tong Wu, Anna Scaglione
We study multi-objective sequential control problems in physical systems whose dynamics and operational constraints are known or can be represented by accurate physics-based models…
Enabling Safety-Critical Wireless Communications via Safe Reinforcement Learning
Haoran Peng, Tong Wu, Hang Liu +3
Ensuring strict safety guarantees is the paramount challenge for emerging 5G/6G wireless systems, particularly as they increasingly govern mission-critical applications ranging fro…
Universal Graph Learning for Power System Reconfigurations: Transfer Across Topology Variations
Tong Wu, Anna Scaglione, Sandy Miguel +1
This work addresses a fundamental challenge in applying deep learning to power systems: developing neural network models that transfer across significant system changes, including…
A Review of Safe Reinforcement Learning Methods for Modern Power Systems
Tong Su, Tong Wu, Junbo Zhao +2
Given the availability of more comprehensive measurement data in modern power systems, reinforcement learning (RL) has gained significant interest in operation and control. Convent…