Newton's Lantern is an RL finetuning pipeline that uses iteration count as reward to produce warm starts for AC power flow, outperforming supervised methods by converging on all tested snapshots with lowest mean iterations on IEEE and GOC benchmarks.
CANOS : A fast and scalable neural AC - OPF solver robust to N-1 perturbations
3 Pith papers cite this work. Polarity classification is still indexing.
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Pith papers citing it
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cs.LG 3years
2026 3representative citing papers
LUMINA-Bench is a standardized evaluation framework for ACOPF surrogate models that tests generalization across multiple grid topologies using accuracy and physics-constraint metrics.
A shared graph neural network framework jointly solves ACOPF and SCUC problems using physics constraints and shows improved generalization to unseen grid topologies.
citing papers explorer
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Newton's Lantern: A Reinforcement Learning Framework for Finetuning AC Power Flow Warm Start Models
Newton's Lantern is an RL finetuning pipeline that uses iteration count as reward to produce warm starts for AC power flow, outperforming supervised methods by converging on all tested snapshots with lowest mean iterations on IEEE and GOC benchmarks.
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LUMINA: A Grid Foundation Model for Benchmarking AC Optimal Power Flow Surrogate Learning
LUMINA-Bench is a standardized evaluation framework for ACOPF surrogate models that tests generalization across multiple grid topologies using accuracy and physics-constraint metrics.
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Towards Systematic Generalization for Power Grid Optimization Problems
A shared graph neural network framework jointly solves ACOPF and SCUC problems using physics constraints and shows improved generalization to unseen grid topologies.