Scalable and Robust Physics-Informed Graph Neural Networks for Water Distribution Systems

• URL: http://arxiv.org/abs/2502.12164v1
• Date: Tue, 11 Feb 2025 13:38:14 GMT
• Title: Scalable and Robust Physics-Informed Graph Neural Networks for Water Distribution Systems
• Authors: Inaam Ashraf, André Artelt, Barbara Hammer,
• Abstract summary: Water distribution systems (WDSs) are an important part of critical infrastructure becoming increasingly significant in the face of climate change and urban growth.<n>We propose a robust and scalable surrogate deep learning (DL) model to enable efficient planning, expansion, and rehabilitation of WDSs.
• Score: 5.067313477651394
• License: http://creativecommons.org/licenses/by-sa/4.0/
• Abstract: 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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