LUMINA: A Grid Foundation Model for Benchmarking AC Optimal Power Flow Surrogate Learning

• URL: http://arxiv.org/abs/2605.02133v1
• Date: Mon, 04 May 2026 01:25:16 GMT
• Title: LUMINA: A Grid Foundation Model for Benchmarking AC Optimal Power Flow Surrogate Learning
• Abstract summary: AC optimal power flow (ACOPF) is foundational yet computationally expensive in power grid operations.<n>We introduce LUMINA-Bench, a benchmark suite for ACOPF surrogate learning covering multi-topology pretraining, transfer, and adaptation.
• Score: 13.448601803960136
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: AC optimal power flow (ACOPF) is foundational yet computationally expensive in power grid operations, driving learning-based surrogates for large-scale grid analysis. These surrogates, however, often fail to generalize across network topologies, a critical gap for deployment on grids not seen during training and for routine operational what-if studies. We introduce LUMINA-Bench, a comprehensive benchmark suite for ACOPF surrogate learning covering multi-topology pretraining, transfer, and adaptation. The benchmark evaluates homogeneous and heterogeneous architectures under single- and multi-topology learning settings using unified metrics that capture both predictive accuracy and physics-informed constraint violations. We additionally compare constraint-aware training objectives, including MSE, augmented Lagrangian, and violation-based Lagrangian losses, to characterize accuracy-robustness trade-offs across settings. Data processing, training, and evaluation frameworks are open-sourced as the LUMINA suite to support reproducibility and accelerate future research on feasibility-aware OPF surrogates.

Related papers

• On Predicting the Post-training Potential of Pre-trained LLMs [60.07459271263409]
  We introduce a new task of predicting post-training potential - forecasting a base model's performance before post-training.<n>We propose RuDE, a unified framework that bypasses the generation gap of base models by leveraging response discrimination.<n>Experiments demonstrate a correlation greater than 90% with post-training performance.
  arXiv  Detail & Related papers  (2026-05-12T11:33:49Z)
• Towards Systematic Generalization for Power Grid Optimization Problems [13.619002117559958]
  AC Optimal Power Flow (ACOPF) and Security-Constrained Unit Commitment (SCUC) are fundamental optimization problems in power system operations.<n>We propose a learning framework that jointly models ACOPF and SCUC through a shared graph-based backbone.
  arXiv  Detail & Related papers  (2026-05-03T19:23:37Z)
• A Graph Foundation Model for Wireless Resource Allocation [37.503398874234094]
  We propose a graph foundation model for resource allocation based on a pre-training and fine-tuning paradigm to extract unified representations.<n>Specifically, we introduce an interference-aware Transformer architecture with a bias projector that injects interference topologies into global attention mechanisms.<n>Experiments demonstrate that the proposed framework achieves state-of-the-art performance and scales effectively with increased model capacity.
  arXiv  Detail & Related papers  (2026-04-08T06:07:28Z)
• Reshaping Action Error Distributions for Reliable Vision-Language-Action Models [69.38615670891038]
  In robotic manipulation, vision-language-action (VLA) models have emerged as a promising paradigm for learning generalizable and scalable robot policies.<n>We focus on continuous-action VLA models and move beyond conventional MSE-based regression by reshaping action error distributions during training.<n>We evaluate our approaches across standard, few-shot, and noisy settings on multiple representative VLA architectures.
  arXiv  Detail & Related papers  (2026-02-04T05:37:09Z)
• Consolidation or Adaptation? PRISM: Disentangling SFT and RL Data via Gradient Concentration [56.074760766965085]
  PRISM achieves a dynamics-aware framework that arbitrates data based on its degree of cognitive conflict with the model's existing knowledge.<n>Our findings suggest that disentangling data based on internal optimization regimes is crucial for scalable and robust agent alignment.
  arXiv  Detail & Related papers  (2026-01-12T05:43:20Z)
• GLOW: Graph-Language Co-Reasoning for Agentic Workflow Performance Prediction [51.83437071408662]
  We propose GLOW, a unified framework for AW performance prediction.<n>GLOW combines the graph-structure modeling capabilities of GNNs with the reasoning power of LLMs.<n>Experiments on FLORA-Bench show that GLOW outperforms state-of-the-art baselines in prediction accuracy and ranking utility.
  arXiv  Detail & Related papers  (2025-12-11T13:30:46Z)
• TopoSizing: An LLM-aided Framework of Topology-based Understanding and Sizing for AMS Circuits [7.615431299673158]
  Traditional black-box optimization achieves sampling efficiency but lacks circuit understanding.<n>We propose TopoSizing, an end-to-end framework that performs robust circuit understanding directly from raw netlists.
  arXiv  Detail & Related papers  (2025-09-17T16:52:46Z)
• QCQP-Net: Reliably Learning Feasible Alternating Current Optimal Power Flow Solutions Under Constraints [4.1920378271058425]
  We propose an innovated computational learning ACOPF, where the input is mapped to the ACOPF network in a computationally efficient manner. We show through simulations that our proposed method achieves superior feasibility rate and cost in situations where the existing-based approaches fail.
  arXiv  Detail & Related papers  (2024-01-11T20:17:44Z)
• Analysis and Optimization of Wireless Federated Learning with Data Heterogeneity [72.85248553787538]
  This paper focuses on performance analysis and optimization for wireless FL, considering data heterogeneity, combined with wireless resource allocation. We formulate the loss function minimization problem, under constraints on long-term energy consumption and latency, and jointly optimize client scheduling, resource allocation, and the number of local training epochs (CRE) Experiments on real-world datasets demonstrate that the proposed algorithm outperforms other benchmarks in terms of the learning accuracy and energy consumption.
  arXiv  Detail & Related papers  (2023-08-04T04:18:01Z)
• Learning Prompt-Enhanced Context Features for Weakly-Supervised Video Anomaly Detection [37.99031842449251]
  Video anomaly detection under weak supervision presents significant challenges. We present a weakly supervised anomaly detection framework that focuses on efficient context modeling and enhanced semantic discriminability. Our approach significantly improves the detection accuracy of certain anomaly sub-classes, underscoring its practical value and efficacy.
  arXiv  Detail & Related papers  (2023-06-26T06:45:16Z)
• Unifying Synergies between Self-supervised Learning and Dynamic Computation [53.66628188936682]
  We present a novel perspective on the interplay between SSL and DC paradigms. We show that it is feasible to simultaneously learn a dense and gated sub-network from scratch in a SSL setting. The co-evolution during pre-training of both dense and gated encoder offers a good accuracy-efficiency trade-off.
  arXiv  Detail & Related papers  (2023-01-22T17:12:58Z)
• Edge-assisted Democratized Learning Towards Federated Analytics [67.44078999945722]
  We show the hierarchical learning structure of the proposed edge-assisted democratized learning mechanism, namely Edge-DemLearn. We also validate Edge-DemLearn as a flexible model training mechanism to build a distributed control and aggregation methodology in regions.
  arXiv  Detail & Related papers  (2020-12-01T11:46:03Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.