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）
Flood models inform strategic disaster management by simulating the spatiotemporal hydrodynamics of flooding. While physics-based numerical flood models are accurate, their substantial computational cost limits their use in operational settings where rapid predictions are essential. Models designed with graph neural networks (GNNs) provide both speed and accuracy while having the ability to process unstructured spatial domains. Given its flexible input and architecture, GNNs can be leveraged alongside physics-informed techniques with ease, significantly improving interpretability and generalizability. We introduce a novel flood GNN architecture, DUALFloodGNN, which embeds physical constraints at both global and local scales through explicit loss terms. The model jointly predicts water volume at nodes and flow along edges through a shared message-passing framework. To improve performance for autoregressive inference, model training is conducted with a multi-step loss enhanced with dynamic curriculum learning. Compared with standard GNN architectures and state-of-the-art GNN flood models, DUALFloodGNN achieves substantial improvements in predicting multiple hydrologic variables (e.g., water volume, flow, and depth) while maintaining high computational efficiency. The model is open sourced at https://github.com/acostacos/dual_flood_gnn. The dataset is open sourced at https://hdl.handle.net/2123/35293 with the DOI 10.25910/9xav-0s86.
Physics-informed GANs for Coastal Flood Visualization
Massachusetts Institute of Technology（麻省理工学院）；Max Planck Institute for Biogeochemistry（马克斯·普朗克生物地球化学研究所）；Spatial Informatics Group（空间信息集团）；ENSTA Institut Polytechnique Paris（巴黎国立高等先进技术学院）；MIT Media Lab（麻省理工学院媒体实验室）；NASA Headquarters（美国国家航空航天局总部）；Latent Sciences（潜在科学公司）；University of Oxford（牛津大学）；Ubisoft（育碧）
Integrating Newton's Laws with deep learning for enhanced physics-informed compound flood modelling
Center for Complex Hydrosystems Research（复杂水系统研究中心）；Department of Civil, Construction and Environmental Engineering（土木、建设与环境工程系）；The University of Alabama（阿拉巴马大学）
Physics-Informed CNN-LSTM for Street-Scale Urban Flood Prediction: Reconciling Aggregate Accuracy and Street-Level Plausibility
University of Virginia（弗吉尼亚大学）；Chandra Robot Autonomy Lab（钱德拉机器人自主实验室）；Link Lab（链接实验室）；Multidisciplinary Research Center（多学科研究中心）；Hydroinformatics Research Group（水文信息学研究组）
Large-scale flood modeling and forecasting with FloodCast
Technical University of Munich（慕尼黑工业大学）；University of Bristol（布里斯托尔大学）；Institute of Mountain Hazards and Environment, Chinese Academy of Sciences（中国科学院成都山地灾害与环境研究所）
Towards Spatio-Temporal Extrapolation of Phase-Field Simulations with Convolution-Only Neural Networks
Sandia National Laboratories（桑迪亚国家实验室）；Lawrence Berkeley National Laboratory（伯克利劳伦斯国家实验室）；University of California, Berkeley（加州大学伯克利分校）；Los Alamos National Laboratory（洛斯阿拉莫斯国家实验室）
Variational Quantum Physics-Informed Neural Networks for Hydrological PDE-Constrained Learning with Inherent Uncertainty Quantification
Faculty of Computer Science and Multimedia, Lincoln University College, Petaling Jaya, Selangor, Malaysia（计算机科学与多媒体学院，林肯大学学院，Petaling Jaya，Selangor，马来西亚）
Downscaling Neural Network for Coastal Simulations
Lappeenranta-Lahti University of Technology LUT（拉普兰塔-拉赫蒂理工大学）；University of Bayreuth（拜鲁特大学）；Norwegian University of Life Sciences（挪威生命科学大学）；Simula Research Laboratory（Simula研究实验室）；Friedrich-Alexander-University Erlangen-Nuremberg（埃尔兰根-纽伦堡弗里德里希-亚历山大大学）；Saarland University（萨尔兰大学）
