用更窄的神经网络实现精准电力流计算，提升电网安全验证效率。
Rethinking Neural Width for Alternating Current Optimal Power Flow Proxies
- 通过渐进式扩容算法自动发现模型最小必要宽度
- 在多个IEEE系统上减少超90%每层神经元数量仍保持精度
- 适合需要高可靠性的电网安全验证场景
深度学习代理模型用于交流最优潮流（ACOPF）缺乏系统性架构规模设计方法。本文通过构建思想实验，回答核心问题：神经网络需多宽才能近乎准确逼近ACOPF流形？提出损失引导神经密集化（LG-ND）算法，仅在当前拓扑无法进一步优化时才逐步扩展容量。实证结果表明，在多个IEEE测试系统中，LG-ND相较文献基准使用最多十倍少的每层神经元即可达到性能相当。这种架构极简性对安全关键型电网运行中的形式化验证至关重要。
原文摘要 · Abstract (English)
Deep learning proxies for Alternating Current Optimal Power Flow (ACOPF) lack systematic methods for determining architectural size. This paper conducts a constructive thought experiment to answer a fundamental inquiry: how wide must a neural network be to almost accurately approximate the ACOPF manifold? We introduce a Loss-Guided Neural Densification (LG-ND) algorithm that incrementally discovers necessary capacity by expanding only when the current deep neural network topology fails to improve further. Empirical results across various IEEE systems show that LG-ND achieves performance parity with literature baselines using up to ten times fewer neurons per layer. Such architectural minimalism is critical for the formal verification required in safety-critical grid operations.
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