面向以边缘为基础的图形神经网络为基础的水分系统的可转移元模型
Bulat Kerimov1, Riccardo Taormina2, Franz Tscheikner-Gratl1
1Department of Civil and Environmental Engineering, Norwegian University of Science and Technology, Trondheim, Norway.
Water research
|July 7, 2024
概括
基于边缘的图形神经网络通过捕捉管道级物理来改善水分系统建模. 这提高了设计和优化水网的可转移性和准确性.
科学领域:
- 液压工程 液压工程 液压工程
- 人工智能的人工智能
- 网络科学 网络科学
背景情况:
- 数据驱动的元模型加速了水分配系统的模拟.
- 由于其图形结构,图形神经网络 (GNN) 显示出对这些系统建模的希望.
- 目前的GNN元模型由于边缘层次的过程表示有限,因此难以转移.
研究的目的:
- 引入基于边缘的图形神经网络 (EB-GNN),以提高元模型的可转移性.
- 改善在管道 (边缘) 层面发生的物理过程的表示.
- 评估EB-GNN与传统GNN对水分系统模拟的性能.
主要方法:
- 开发了一个EB-GNN架构,结合了边缘级物理过程表示.
- 模拟了用于水分网络的稳定状态EPANET模拟.
- 在准确性和加快速度的基准系统上比较EB-GNNs和传统GNNs.
- 通过在未见网络拓上测试模型来评估可转移性.
主要成果:
- 与基于节点的GNN相比,EB-GNN更准确地捕获管道级物理过程.
- 在看不见的网络上实现了高通用化性能 (流速为R2高达0.98,头为0.95).
- 与传统基于物理的模拟相比,证明了显著的加快速度.
结论:
- EB-GNN 提供了更好的准确性和可转移性,用于配水系统的元建模.
- 拟议的架构有效地代表了链接层次的动态,解决了传统GNN的局限性.
- EB-GNN显示了有效设计,控制和优化水网的潜力,特别是在数据稀缺或大量布局评估的情况下.
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01:12Modeling and Similitude
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01:21Multiple Pipe Systems
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01:17Design Example: Analyzing Capacity Contours for Flood Risk Assessment
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01:29Gradually Varying Flow
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