Water distribution systems (WDS) are an integral part of critical infrastructure which is pivotal to urban development. As 70% of the world's population will likely live in urban environments in 2050, efficient simulation and planning tools for WDS play a crucial role in reaching UN's sustainable developmental goal (SDG) 6 - "Clean water and sanitation for all". In this realm, we propose a novel and efficient machine learning emulator, more precisely, a physics-informed deep learning (DL) model, for hydraulic state estimation in WDS. Using a recursive approach, our model only needs a few graph convolutional neural network (GCN) layers and employs an innovative algorithm based on message passing. Unlike conventional machine learning tasks, the model uses hydraulic principles to infer two additional hydraulic state features in the process of reconstructing the available ground truth feature in an unsupervised manner. To the best of our knowledge, this is the first DL approach to emulate the popular hydraulic simulator EPANET, utilizing no additional information. Like most DL models and unlike the hydraulic simulator, our model demonstrates vastly faster emulation times that do not increase drastically with the size of the WDS. Moreover, we achieve high accuracy on the ground truth and very similar results compared to the hydraulic simulator as demonstrated through experiments on five real-world WDS datasets.
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）
AgriPINN: A Process-Informed Neural Network for Interpretable and Scalable Crop Biomass Prediction Under Water Stress
Department of Computing, and Mathematics, Faculty of Science and Engineering（计算与数学系，科学与工程学院）；Manchester Metropolitan University（曼彻斯特 Metropolitan 大学）；Leibniz Centre for Agricultural Landscape Research (ZALF)（莱比锡农业景观研究中心（ZALF））；Institute of Crop Science and Resource Conservation (INRES)（作物科学与资源保护研究所）
Conformal mapping based Physics-informed neural networks for designing neutral inclusions
Department of Mathematical Sciences, Korea Advanced Institute of Science and Technology（韩国科学技术院数学科学系）；Department of Artificial Intelligence, Chung-Ang University（Chung-Ang 大学人工智能系）
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）
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）
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）
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）
Partition of Unity Physics-Informed Neural Networks (POU-PINNs): An Unsupervised Framework for Physics-Informed Domain Decomposition and Mixtures of Experts
The University of Texas at El Paso（德克萨斯大学埃尔帕索分校）；University of California, Santa Cruz（加州大学圣克鲁兹分校）；Clarkson University（克拉克森大学）；Texas A&M University in Kingsville（德克萨斯农工大学金斯维尔分校）
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）
Reservoir History Matching of the Norne field with generative exotic priors and a coupled Mixture of Experts -- Physics Informed Neural Operator Forward Model
PIDT: Physics-Informed Digital Twin for Optical Fiber Parameter Estimation
Dept. of Electrical Engineering, Chalmers Univ. of Technology, Sweden（电气工程系，瑞典查尔姆斯理工大学）；Dept. of Microtechnology and Nanoscience, Chalmers Univ. of Technology, Sweden（微电子与纳米科技系，瑞典查尔姆斯理工大学）
