5 papers
E-Globe: Scalable -Global Verification of Neural Networks via Tight Upper Bounds and Pattern-Aware Branching
Wenting Li, Saif R. Kazi, Russell Bent +2
Neural networks achieve strong empirical performance, but robustness concerns still hinder deployment in safety-critical applications. Formal verification provides robustness guara…
Real-Time Dynamic N-1 Screening: Identifying High-Risk Lines and Transformers After Common Faults
Ayrton Almada, Laurent Pagnier, Igal Goldshtein +3
Power system operators routinely perform N-1 contingency analysis, yet conventional tools provide limited guidance on which lines or transformers deserve heightened attention durin…
Real-Time Stochastic Assessment of Dynamic N-1 Grid Contingencies
Ayrton Almada, Laurent Pagnier, Igal Goldshtein +3
Power system operators need tools for rapid, real-time counterfactual assessments of grid security under fast-changing conditions. Traditional N-1 contingency analysis lacks dynami…
Optimal Trajectory Planning for Space Object Tracking with Collision-Avoidance Constraints
Saif R. Kazi, Harsha Nagarajan, Hassan Hijazi +1
A control optimization approach is presented for a chaser spacecraft tasked with maintaining proximity to a target space object while avoiding collisions. The target object traject…
LEVIS: Large Exact Verifiable Input Spaces for Neural Networks
Mohamad Fares El Hajj Chehade, Wenting Li, Brian W. Bell +3
The robustness of neural networks is crucial in safety-critical applications, where identifying a reliable input space is essential for effective model selection, robustness evalua…