Finite-Sample Probabilistic Safety Certification for AI-Based Grid-Edge Coordination

arXiv:2609.28182 · cs.AI, cs.LG, cs.SY, eess.SY · Submitted 2026-09-23 · Read on arXiv

cs.AI, cs.LG, cs.SY, eess.SY

Submitted: 2026-09-23

Updated: 2026-09-23

Terminology

• Calibration — 51× in this paper · explained in 7 episodes
• Verification — 45× in this paper · explained in 2 episodes
• Reinforcement Learning — 9× in this paper · explained in 4 episodes
• Adversarial Attacks — 6× in this paper · explained in 1 episode
• Robustness — 6× in this paper · explained in 5 episodes
• Distribution Shift — 5× in this paper · explained in 5 episodes
• Core — 4× in this paper · explained in 2 episodes
• Formal Verification — 4× in this paper · explained in 2 episodes
• Self-Supervised Learning — 3× in this paper · explained in 7 episodes
• AI Agents — 2× in this paper · explained in 2 episodes
• Conformal Prediction — 2× in this paper · explained in 5 episodes
• Hybrid System — 2× in this paper · explained in 2 episodes
• Interpretability — 2× in this paper · explained in 15 episodes
• Sim-to-Real Gap — 2× in this paper · explained in 6 episodes
• alpha — 2× in this paper · explained in 1 episode
• Adversarial Examples — 1× in this paper · explained in 2 episodes
• Benchmark — 1× in this paper · explained in 2 episodes
• Cooperation — 1× in this paper · explained in 2 episodes
• Deep Reinforcement Learning — 1× in this paper · explained in 2 episodes
• Edge Devices — 1× in this paper · explained in 2 episodes
• Epistemic Uncertainty — 1× in this paper · explained in 9 episodes
• Explainability — 1× in this paper · explained in 3 episodes
• Foundation Model — 1× in this paper · explained in 3 episodes
• Foundation Models — 1× in this paper · explained in 7 episodes
• International Energy Agency — 1× in this paper
• Lyapunov function — 1× in this paper · explained in 2 episodes
• Reinforcement Learning (RL) — 1× in this paper · explained in 25 episodes
• Trace — 1× in this paper · explained in 2 episodes

Sources

• GradMAP: Gradient-Based Multi-Agent Proximal Learning for Grid-Edge Flexibility
• TorchLean: Formalizing Neural Networks in Lean
• Scalable Exact Verification of Optimization Proxies for Large-Scale Optimal Power Flow
• The 6th International Verification of Neural Networks Competition (VNN-COMP 2025): Summary and Results
• Decision-calibrated prediction sets for robust power system operations
• Verification and Validation of Physics-Informed Surrogate Component Models for Dynamic Power-System Simulation
• Trustworthiness Layer for Foundation Models in Power Systems: Application to N-k Contingency Screening
• Explaining and Harnessing Adversarial Examples

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