Signals 4 · free daily AI digest

Towards Hierarchical GNNs for multi-grid power flow: generalization across operating scenarios

Paper recorded by Signals 4 on 2026-09-22 in cs.AI. Abstract reproduced from arXiv; link to the original below.

Published 2026-09-22 on arXiv · recorded by Signals 4 on 2026-09-23

Category: cs.AI · 人工智能 · first seen 2026-09-23

Abstract

Hierarchical latent communication improves the generalization of a multi-grid power-flow model to new operating scenarios. The module exchanges information through two reduced graphs within a GENCO-based corrective network. We compare Kron-derived transports, a same-anchor Quotient construction and a flat backbone in preliminary trainings of 200 epochs on three grid topologies, with three initiali

Read on arXiv →

#20 most recent of 360 cs.AI papers we have recorded · ↑ newer: Greedy Decoding Is Not Precision-Invariant: Cross-Precision Output Div · ↓ older: GameHorizon Suite: Multi-Horizon Data and Evaluation in Gameplay
Cite this page: Towards Hierarchical GNNs for multi-grid power flow: generalization across operating scenarios: the #20 most recent of 360 cs.AI papers we have recorded (as of 2026-09-22). Source: Signals 4 (Signals API) — https://data.jiangzhang.ca/signals4/t/papers/towards-hierarchical-gnns-for-multi-grid-power-flow-generalization-across-operat.html
Free to quote with attribution to “Signals 4 (Signals API)”. Machine-readable: papers.json
Related: More cs.AI papers · arXiv signals · All papers · Today in AI
Get 4 AI signals a day by email — free.
Subscribe free → See all plans →
Get 4 AI signals a day by email — free
All models · All repos · By company · Daily editions