# LEAP nets for power grid perturbations

arXiv: https://arxiv.org/abs/1908.08314 · HTML: https://mlsanity.com/paper/1908.08314/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 22 Aug 2019.

## Built on by (most cited first, 3 of 3)

- [Multi-fidelity power flow solver](https://mlsanity.com/paper/2205.13362/) (2022)
- [Machine Learning for Physical Simulation Challenge Results and Retrospective Analysis: Power Grid Use Case](https://mlsanity.com/paper/2505.01156/) (2025)
- [Study Design and Demystification of Physics Informed Neural Networks for Power Flow Simulation](https://mlsanity.com/paper/2509.19233/) (2025)

## Abstract (arXiv)

We propose a novel neural network embedding approach to model power transmission grids, in which high voltage lines are disconnected and reconnected with one-another from time to time, either accidentally or willfully. We call our architeture LEAP net, for Latent Encoding of Atypical Perturbation. Our method implements a form of transfer learning, permitting to train on a few source domains, then generalize to new target domains, without learning on any example of that domain. We evaluate the viability of this technique to rapidly assess cu-rative actions that human operators take in emergency situations, using real historical data, from the French high voltage power grid.