Scaling Laws for Physics-Aware ACOPF Surrogate Learning
cs.LG
Submitted: 2026-09-14
Updated: 2026-09-26
License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
Terminology
Sources
- CANOS: A Fast and Scalable Neural AC-OPF Solver Robust To N-1 Perturbations
- Scaling Laws for Neural Language Models
- LUMINA: A Grid Foundation Model for Benchmarking AC Optimal Power Flow Surrogate Learning
- LUMINA: Foundation Models for Topology Transferable ACOPF
- Deep Learning Scaling is Predictable, Empirically
- Scaling Vision Transformers
- Towards Foundation Models for Scientific Machine Learning: Characterizing Scaling and Transfer Behavior
- Towards Neural Scaling Laws on Graphs
- Scaling Laws of Graph Neural Networks for Atomistic Materials Modeling
- Semi-Supervised Classification with Graph Convolutional Networks
- Graph Attention Networks
- OPFData: Large-scale datasets for AC optimal power flow with topological perturbations
- PGLearn -- An Open-Source Learning Toolkit for Optimal Power Flow
- Data-driven AC Optimal Power Flow with Physics-informed Learning and Calibrations
- An Augmented Lagrangian Method on GPU for Security-Constrained AC Optimal Power Flow
- MadNCL: A GPU Implementation of Algorithm NCL for Large-Scale, Degenerate Nonlinear Programs
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