构建可迁移的电力系统优化模型，让算法既懂物理规律又高效求解。
LUMINA: Foundation Models for Topology Transferable ACOPF
- 基于电网优化问题提炼出三类科学模型设计原则
- 在满足物理约束前提下实现跨场景高精度求解
- 适合电力系统、工业优化等需遵守物理规律的领域
通用基础模型有望通过学习可复用表征加速科学计算，但受限于必须满足物理规律与安全约束的科学系统，传统训练范式面临挑战。本文以交流最优潮流（ACOPF）为例，系统研究电力系统运行中的代表性优化问题，其功率平衡方程与运行约束不可妥协。通过控制实验考察不同架构、训练目标与系统多样性，提取出三条经验性设计原则，涵盖：学习物理不变表征同时尊重系统特异性约束、优化精度与保证约束满足之间的权衡、在高影响运行状态下的可靠性保障。提出LUMINA框架，包含数据处理与训练流程，支持跨科学应用中物理感知、可行性感知的基础模型可复现研究。
原文摘要 · Abstract (English)
Foundation models in general promise to accelerate scientific computation by learning reusable representations across problem instances, yet constrained scientific systems, where predictions must satisfy physical laws and safety limits, pose unique challenges that stress conventional training paradigms. We derive design principles for constrained scientific foundation models through systematic investigation of AC optimal power flow (ACOPF), a representative optimization problem in power grid operations where power balance equations and operational constraints are non-negotiable. Through controlled experiments spanning architectures, training objectives, and system diversity, we extract three empirically grounded principles governing scientific foundation model design. These principles characterize three design trade-offs: learning physics-invariant representations while respecting system-specific constraints, optimizing accuracy while ensuring constraint satisfaction, and ensuring reliability in high-impact operating regimes. We present the LUMINA framework, including data processing and training pipelines to support reproducible research on physics-informed, feasibility-aware foundation models across scientific applications.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。