Reconstructing transient states presents significant challenges, particularly within complex pipe networks. These challenges arise due to nonlinear behaviours, inherent uncertainties in the system, and limitations in data availability. This work proposed a novel approach employing Physics-Informed Neural Networks (PINN) to reconstruct transient states in pipe networks, even with limited sensor data. To integrate the complex topology of pipe network systems into neural networks, the method integrates the PINN framework with an efficient elastic water column (EWC) model which can be simply formulated across diverse pipe network configurations. The results showed the proposed PINN method can accurately reconstruct the pressure and flow variation at unmonitored locations, even provided with noisy data at a limited number of locations. One of its advantages lies in its ability to effectively capture extreme values that hold potential significance for pipe infrastructure, providing a promising avenue for pipe failure analysis and enhanced safety management. Laboratory experiments have also been conducted to validate the efficacy and reliability of this method, thus further underlining its potential for real-world applications.

中文翻译：

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使用物理信息神经网络根据局部观测重建管网中的瞬态压力

重建瞬态带来了巨大的挑战，特别是在复杂的管网中。这些挑战的出现是由于非线性行为、系统固有的不确定性以及数据可用性的限制。这项工作提出了一种采用物理信息神经网络（PINN）的新颖方法来重建管道网络中的瞬态，即使传感器数据有限。为了将管网系统的复杂拓扑集成到神经网络中，该方法将 PINN 框架与高效的弹性水柱 (EWC) 模型集成，该模型可以在不同的管网配置中简单地制定。结果表明，即使在有限数量的位置提供了噪声数据，所提出的 PINN 方法也可以准确地重建未监控位置的压力和流量变化。其优势之一在于能够有效捕获对管道基础设施具有潜在意义的极值，为管道故障分析和增强安全管理提供了一种有前途的途径。还进行了实验室实验来验证该方法的有效性和可靠性，从而进一步强调其在实际应用中的潜力。