用物理约束的图神经网络，高效模拟供水系统并提升泛化能力。
Scalable and Robust Physics-Informed Graph Neural Networks for Water Distribution Systems
- 融合物理规律的图神经网络架构，结合创新训练与归一化方法。
- 在多个供水系统上表现优于现有最先进模型，且可扩展至更大系统。
- 对需求、管径等分布外输入更鲁棒，适合真实复杂场景应用。
供水系统（WDS）是关键基础设施的重要组成部分，在气候变化和城市人口增长背景下愈发重要。本文提出一种稳健且可扩展的替代深度学习模型，以支持供水系统的高效规划、扩建与改造。所提方法结合改进的图神经网络架构、适配的物理信息算法、创新训练策略及物理保持的数据归一化方法。在多个供水系统上的评估结果表明，该模型性能超越当前最先进的深度学习模型。此外，本方法可将模型扩展至更大、更真实的供水系统。同时，所提方法显著提升了模型对分布外输入特征（如用水需求、管道直径）的鲁棒性。因此，该方法为缩小人工智能在供水系统中从仿真到实际应用的差距迈出了重要一步。
原文摘要 · Abstract (English)
Water distribution systems (WDSs) are an important part of critical infrastructure becoming increasingly significant in the face of climate change and urban population growth. We propose a robust and scalable surrogate deep learning (DL) model to enable efficient planning, expansion, and rehabilitation of WDSs. Our approach incorporates an improved graph neural network architecture, an adapted physics-informed algorithm, an innovative training scheme, and a physics-preserving data normalization method. Evaluation results on a number of WDSs demonstrate that our model outperforms the current state-of-the-art DL model. Moreover, our method allows us to scale the model to bigger and more realistic WDSs. Furthermore, our approach makes the model more robust to out-of-distribution input features (demands, pipe diameters). Hence, our proposed method constitutes a significant step towards bridging the simulation-to-real gap in the use of artificial intelligence for WDSs.
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