MxGPS: Multiplex Graph Transformers for a Power Grid Foundation Model
Single-task fine-tuning of graph neural networks (GNNs) for power grid problems exhibits a systematic failure mode: models that achieve the lowest in-distribution error degrade the most under topology shift. We term this topology overfitting: the tendency of task-specific gradient signals to encode relational structure particular to the training topologies rather than the underlying physics, causing models to fail on unseen grids despite strong in-distribution performance. To expose and address this failure mode, we introduce MxGPS (Multiplex GPS), a multiplex graph transformer that runs K task-specialised GPS branches over a shared node encoder, jointly trained on Static State Estimation...
| Authors | Charilaos Papaioannou, Ioannis Tsantilas, Dimitris Giannakakos et al. |
|---|---|
| Primary category | cs.LG |
| Published | 15 July 2026 |
| Last updated at source | 17 July 2026 |
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