surrogate-learning

This paper proposes Learning-to-Optimize as a missing architectural layer for AI-native networks, redefining optimization algorithms as offline knowledge generators to train surrogate models for efficient real-time inference in dynamic environments.

This paper studies scaling laws for physics-aware surrogate models in AC optimal power flow, demonstrating that physics-informed training objectives improve constraint satisfaction at scale with different scaling rates than standard loss functions.