# Neural Networks for AC Optimal Power Flow: Improving Worst-Case Guarantees during Training

arXiv: https://arxiv.org/abs/2510.23196 · HTML: https://mlsanity.com/paper/2510.23196/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 27 Oct 2025.

## Built on by (most cited first, 1 of 1)

- [Generating adversarial inputs for a graph neural network model of AC power flow](https://mlsanity.com/paper/2602.17975/) (2026)

## Abstract (arXiv)

The AC Optimal Power Flow (AC-OPF) problem is central to power system operation but challenging to solve efficiently due to its nonconvex and nonlinear nature. Neural networks (NNs) offer fast surrogates, yet their black-box behavior raises concerns about constraint violations that can compromise safety. We propose a verification-informed NN framework that incorporates worst-case constraint violations directly into training, producing models that are both accurate and provably safer. Through post-hoc verification, we achieve substantial reductions in worst-case violations and, for the first time, verify all operational constraints of large-scale AC-OPF proxies. Practical feasibility is further enhanced via restoration and warm-start strategies for infeasible operating points. Experiments on systems ranging from 57 to 793 buses demonstrate scalability, speed, and reliability, bridging the gap between ML acceleration and safe, real-time deployment of AC-OPF solutions - and paving the way toward data-driven optimal control.