@unpublished{yang2026gridsfm,
author = {Yang, Weiwei and Britto Mattos Lima, Andrea and Spina, Thiago V. and Fowers, Spencer and Zhang, Baosen   and White, Chris},
title = {GridSFM: A Foundation Model for AC Optimal Power Flow},
year = {2026},
month = {May},
abstract = {Grid Small Foundation Model (GridSFM) is a foundation model for power systems trained on 200 grids and over half a million scenarios. It predicts AC-OPF solutions in milliseconds: given a grid topology and loading conditions, it produces bus voltages, generator dispatch, branch power flows, and a feasibility classification without running a solver, and when higher confidence is needed its predictions serve as warm starts that accelerate conventional solvers. GridSFM is released in two tiers, GridSFM-Open (∼15M parameters, for grids up to a few thousand buses, research and prototyping) and GridSFM-Premier (∼100M parameters, for production scale grids up to tens of thousands of buses), both sharing the same architecture and trained on a broad open-data corpus of transmission topologies and operating scenarios spanning feasible and infeasible regimes via a multi-axis perturbation pipeline. Code is available at https://github.com/microsoft/GridSFM, and released checkpoints are hosted in the Hugging Face collection at https://huggingface.co/collections/microsoft/gridsfm.},
url = {http://approjects.co.za/?big=en-us/research/publication/gridsfm-a-foundation-model-for-ac-optimal-power-flow/},
}