Author

Massimiliano Lupo Pasini

2 papers indexed here

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#artificial intelligence May 2026

Scalable Heterogeneous Graph Foundation Models for Data-Driven Optimal Power Flow in Smart Grids

A large-scale heterogeneous graph-learning workflow, built on HydraGNN, for data-driven OPF surrogate modeling and graph foundation-model (GFM) development and shows that partial fine-tuning provides the strongest balance between predictive performance and computational requirements.

Massimiliano Lupo Pasini, Yi-Jiang Li, Kibaek Kim et al. · arXiv.org · 2 citations

#machine learning Preprint Sep 2026

Scaling Laws for Physics-Aware ACOPF Surrogate Learning

The training objective shapes not only where a surrogate lands but how its quality evolves with scale, and how violation changes with network size is measured.

Yi-Jiang Li, Emon Dey, Stefano Fenu et al. · 0 citations