水和废水系统中的物理信息神经网络:批判性审查
Antonino Di Bella1, Maziar Raissi2, Domenico Santoro3
1Department of Civil Engineering and Architecture, University of Catania, Catania, Italy.
Water research
|February 4, 2026
概括
物理信息神经网络 (PINNs) 通过将物理定律集成到AI中来增强水系统建模. 它们提高了水和废水管理中的参数估计和系统识别数据的效率和准确性.
科学领域:
- 环境工程 环境工程
- 计算科学 计算科学
- 人工智能的人工智能
背景情况:
- 物理信息神经网络 (PINNs) 将物理定律 (PDEs) 与神经网络合并.
- 这种混合方法尊重保护原则,并从有限的数据中学习.
- PINNs为科学机器学习提供了一个新的范式.
研究的目的:
- 批判性地审查PINN在水和废水系统中的应用 (2014-2024年).
- 确定PINNs在这个领域的优势,局限性和未来潜力.
- 评估PINNs在解决水基础设施管理方面的挑战中的作用.
主要方法:
- 在饮用水,废水处理,城市排水和水处理中对PINN应用的系统文献综述.
- 分析报告的绩效指标,数据要求和概括能力.
- 评估有关复杂系统的局限性,趋同和不确定性量化.
主要成果:
- 在反向问题,参数估计和系统识别方面,PINN表现出强的表现.
- 与标准神经网络相比,训练数据显著减少 (3-30倍).
- PINNs显示了更好的概括性,特别是在分布转移和部分观测下.
结论:
- PINNs是水基础设施的宝贵补充工具,桥梁机械和数据驱动的方法.
- 他们擅长参数校准,传感器放置和实时状态估计.
- 复杂系统,融合和不确定性量化需要进一步开发.
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