Physics-informed GNN for medium-high voltage AC power flow with edge-aware attention and line search correction operator

Excerpt

arXiv:2509.22458v3 Announce Type: replace-cross Abstract: Physics-informed graph neural networks (PIGNNs) have emerged as fast AC power-flow solvers that can replace the classic NewtonRaphson (NR) solvers, especially when thousands of scenarios must be evaluated. However, current PIGNNs still need accuracy improvements at parity speed; in particular, the soft constraint on the physics loss is inoperative at inference, which can deter operational adoption. We address this with PIGNN-Attn-LS, comb