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

Kari Jaaskelainen

29 Sep 2025 — 1 min read

Edge-aware attention plus line-search correction for fast, accurate AC power flow

Power systems planners need solvers that are both fast and faithful to physics. This work advances Physics-Informed GNNs with two key innovations. First, an edge-aware attention mechanism injects line physics via per-edge biases, capturing grid anisotropy that standard MLP-based message passing overlooks. Second, a backtracking line-search correction operator restores an operative decrease criterion at inference, ensuring the physics-informed loss meaningfully guides solutions beyond training.

Trained and tested with a realistic High-/Medium-Voltage scenario generator (with Newton–Raphson used only for references), the proposed PIGNN-Attn-LS delivers large accuracy gains at parity speed. On held-out HV grids (4–32 buses), it achieves 0.00033 p.u. RMSE in voltage magnitude and 0.08° in angle—outperforming a PIGNN-MLP baseline by 99.5% and 87.1%, respectively. With streaming micro-batches, it provides 2–5× faster batched inference than Newton–Raphson on 4–1024-bus networks.

Edge-aware attention: encodes line physics and anisotropy via per-edge biases.
Line-search correction: globalized operator enforces progress at inference.
High fidelity: near-NR accuracy with robust generalization across grid sizes.
Throughput: significant speedups with batched, streaming inference.

For operators and tool vendors, this points to practical deployment of physics-informed ML as a front-line AC power-flow solver when thousands of scenarios must be evaluated rapidly—without giving up numerical trust.

Paper: http://arxiv.org/abs/2509.22458v1
Register: https://www.AiFeta.com

#PowerSystems #GNN #PhysicsInformed #Optimization #SmartGrid #ACPowerFlow #EnergyAI

From f(x) and g(x) to f(g(x)): LLMs Learn New Skills in RL by Composing Old Ones

Evidence that RL teaches genuinely new abilities: compositional skills emerge and transfer across tasks Does RL merely reweight what an LLM already knows—or can it teach genuinely new skills? This paper offers concrete evidence for the latter. Using a controlled, synthetic framework, the authors define “skills” as string transformation

OpenGPT-4o-Image: A Comprehensive Dataset for Advanced Image Generation and Editing

A structured 80k instruction–image corpus spanning 11 domains and 51 subtasks to train unified visual editors Unified models for image generation and editing hit a data ceiling: existing corpora emphasize basic manipulations but miss real‑world complexity. OpenGPT‑4o‑Image tackles this with a hierarchical task taxonomy and automated

Random Policy Valuation is Enough for LLM Reasoning with Verifiable Rewards

ROVER replaces PPO loops with uniform‑policy Q‑values—boosting quality and diversity in math reasoning Popular RLVR methods for LLM reasoning lean on generalized policy iteration (e.g., PPO/GRPO), but suffer instability and diversity collapse. This paper reframes math RLVR as a specialized finite‑horizon MDP with deterministic

CLPO: Curriculum Learning meets Policy Optimization for LLM Reasoning

A dynamic, self‑paced curriculum that restructures problems to match model ability in RLVR Online RL with Verifiable Rewards (RLVR) has boosted LLM reasoning—but most methods treat all problems equally, wasting effort on solved items and flailing on those beyond current capability. CLPO fixes that with a dynamic pedagogy:

Read more

From f(x) and g(x) to f(g(x)): LLMs Learn New Skills in RL by Composing Old Ones

OpenGPT-4o-Image: A Comprehensive Dataset for Advanced Image Generation and Editing

Random Policy Valuation is Enough for LLM Reasoning with Verifiable Rewards

CLPO: Curriculum Learning meets Policy Optimization for LLM Reasoning