Detecting and localizing leakages is a significant challenge for the efficient and sustainable management of water distribution networks (WDN). Leveraging the inherent graph structure of WDNs, recent approaches have used graph-based data-driven methods. However, these methods often learn shortcuts that work well with in-distribution data but fail to generalize to out-of-distribution data. To address this limitation and inspired by the perfect generalization ability of classical algorithms, we propose an algorithm-informed graph neural network (AIGNN). Recognizing that WDNs function as flow networks, incorporating max-flow information can be beneficial for inferring pressures. In the proposed framework, we first train AIGNN to emulate the Ford-Fulkerson algorithm for solving max-flow problems. This algorithmic knowledge is then transferred to address the pressure estimation problem in WDNs. Two AIGNNs are deployed, one to reconstruct pressure based on the current measurements, and another to predict pressure based on previous measurements. Leakages are detected and localized by comparing the outputs of the reconstructor and the predictor. By pretraining AIGNNs to reason like algorithms, they are expected to extract more task-relevant and generalizable features. Experimental results demonstrate that the proposed algorithm-informed approach achieves superior results with better generalization ability compared to GNNs that do not incorporate algorithmic knowledge.
The impact of sensor placement on graph-neural-network-based leakage detection
Control Systems Group, Dept. of Electrical Engineering, Eindhoven University of Technology（埃因霍温理工大学控制系统组）；Wetsus, Centre of Excellence for Sustainable Water Technology（可持续水技术卓越中心）；Statistical Methods – Biometris, Wageningen University（瓦格宁根大学统计方法—生物计量学）
DUALFloodGNN: Physics-informed Graph Neural Network for Operational Flood Modeling
Department of Computer Science, School of Computing, National University of Singapore（新加坡国立大学计算机科学系）；School of Civil Engineering, Faculty of Engineering, The University of Sydney（悉尼大学土木工程学院）；Delft Institute of Applied Mathematics, Delft University of Technology（代尔夫特理工大学应用数学研究所）
Unsupervised Online Detection of Pipe Blockages and Leakages in Water Distribution Networks
KIOS Research and Innovation Center of Excellence, University of Cyprus（希腊塞浦路斯大学KIOS研究中心）；Department of Electrical and Computer Engineering, University of Cyprus（塞浦路斯大学电气与计算机工程系）
Informed Learning for Estimating Drought Stress at Fine-Scale Resolution Enables Accurate Yield Prediction
German Research Center for Artificial Intelligence（德国人工智能研究中心）；Department of Computer Science, University of Kaiserslautern-Landau（科隆-兰道大学计算机科学系）；Bernoulli Institute, University of Groningen（格罗宁根大学伯努利学院）
Finite Volume-Informed Neural Network Framework for 2D Shallow Water Equations: Rugged Loss Landscapes and the Importance of Data Guidance
Department of Civil and Environmental Engineering, Pennsylvania State University, University Park, PA, USA（宾夕法尼亚州立大学土木与环境工程系，大学公园，PA，美国）；Institute for Computational and Data Sciences, Pennsylvania State University, University Park, PA, USA（宾夕法尼亚州立大学计算与数据科学研究所，大学公园，PA，美国）
Data assimilation and parameter identification for water waves using the nonlinear Schrödinger equation and physics-informed neural networks
Hamburg University of Technology（汉堡工业大学）；TU Dortmund University（多特蒙德工业大学）；Technical University of Munich（慕尼黑工业大学）；German Aerospace Center（德国航空航天中心）；Imperial College London（伦敦帝国理工学院）；Technische Universität Berlin（柏林工业大学）
Lagged backward-compatible physics-informed neural networks for unsaturated soil consolidation analysis
George Mason University（乔治梅森大学）；National Institute of Natural Hazards, Ministry of Emergency Management（应急管理部国家自然灾害防治研究院）；Northwest Institute of Eco-Environment and Resources, Chinese Academy of Sciences（中国科学院西北生态环境资源研究院）；École Nationale des Ponts et Chaussées（巴黎路桥学院）；Hebei University of Engineering（河北工程大学）；Zhejiang University of Technology（浙江工业大学）
Learning characteristic parameters and dynamics of centrifugal pumps under multiphase flow using physics-informed neural networks
Faculdade de Engenharia Mecânica (FEM), Universidade Estadual de Campinas (UNICAMP)（坎皮纳斯州立大学机械工程学院）；Centro de Estudos de Energia e Petróleo (CEPETRO), Universidade Estadual de Campinas (UNICAMP)（坎皮纳斯州立大学能源与石油研究中心）；Division of Applied Mathematics, Brown University（布朗大学应用数学系）；School of Engineering, Brown University（布朗大学工程学院）
A Deep Learning Approach for Predicting Two-dimensional Soil Consolidation Using Physics-Informed Neural Networks (PINN)
School of Engineering and Technology, China University of Geosciences (Beijing)（中国地质大学（北京）工程技术学院）；Department of Mathematics and Applications, University of Naples Federico II, Italy（意大利那不勒斯费德里科二世大学数学与应用数学系）
Physics-informed extreme learning machine for Terzaghi consolidation problems and interpretation of coefficient of consolidation based on CPTu data
Shandong University（山东大学）；China University of Mining and Technology（中国矿业大学）；Qilu University of Technology (Shandong Academy of Sciences)（齐鲁工业大学（山东省科学院））；University of Warwick（华威大学）；University of Leeds（利兹大学）
AviaSafe: A Physics-Informed Data-Driven Model for Aviation Safety-Critical Cloud Forecasts
Artificial Intelligence Innovation and Incubation Institute, Fudan University（复旦大学人工智能创新与孵化院）；Shanghai Innovation Institute（上海创新研究院）；Shanghai Academy of Artificial Intelligence for Science（上海人工智能科学研究院）；National Marine Environment Forecasting Center（国家海洋环境预报中心）；Department of Atmospheric and Oceanic Sciences, Fudan University（复旦大学大气科学与海洋科学系）
Reservoir History Matching of the Norne field with generative exotic priors and a coupled Mixture of Experts -- Physics Informed Neural Operator Forward Model
Simulation-informed deep learning for enhanced SWOT observations of fine-scale ocean dynamics
IMT Atlantique（大西洋高等电信学院）；CNRS（法国国家科学研究中心）；INRIA（法国国家信息与自动化研究所）；Ifremer（法国海洋开发研究院）；Jet Propulsion Laboratory（喷气推进实验室）；California Institute of Technology（加州理工学院）
Dual Unscented Kalman Filter Architecture for Sensor Fusion in Water Networks Leak Localization
Institut de Robòtica i Informàtica Industrial, CSIC-UPC（西班牙科学研究理事会-加泰罗尼亚理工大学机器人与工业自动化研究所）；LOS-CS-FMI, University of Bucharest（布加勒斯特大学数学与计算机科学学院计算机科学系）；Supervision, Safety and Automatic Control Research Center (CS2AC) of the Universitat Politècnica de Catalunya（加泰罗尼亚理工大学监督、安全与自动控制研究中心）；Politehnica University of Bucharest（布加勒斯特理工大学）
