Foundation models can solve complex physics optimization problems across different system sizes by combining pretraining on diverse topologies with physics-informed fine-tuning, enabling practical deployment on real power grids without retraining from scratch.
GridSFM is a foundation model that solves AC Optimal Power Flow (a critical power grid optimization problem) by pretraining a 15M-parameter graph neural network across 54 different grid topologies, then fine-tuning it with physics-informed methods.