Physics simulation results of natural processes usually do not fully capture the real world. This is caused for instance by limits in what physical processes are simulated and to what accuracy. In this work we propose and analyze the use of an LSTM-based deep learning network machine learning (ML) architecture for capturing and predicting the behavior of the systemic error for storm surge forecast models with respect to real-world water height observations from gauge stations during hurricane events. The overall goal of this work is to predict the systemic error of the physics model and use it to improve the accuracy of the simulation results post factum. We trained our proposed ML model on a dataset of 61 historical storms in the coastal regions of the U.S. and we tested its performance in bias correcting modeled water level data predictions from hurricane Ian (2022). We show that our model can consistently improve the forecasting accuracy for hurricane Ian -- unknown to the ML model -- at all gauge station coordinates used for the initial data. Moreover, by examining the impact of using different subsets of the initial training dataset, containing a number of relatively similar or different hurricanes in terms of hurricane track, we found that we can obtain similar quality of bias correction by only using a subset of six hurricanes. This is an important result that implies the possibility to apply a pre-trained ML model to real-time hurricane forecasting results with the goal of bias correcting and improving the produced simulation accuracy. The presented work is an important first step in creating a bias correction system for real-time storm surge forecasting applicable to the full simulation area. It also presents a highly transferable and operationally applicable methodology for improving the accuracy in a wide range of physics simulation scenarios beyond storm surge forecasting.
Storm Surge in Color: RGB-Encoded Physics-Aware Deep Learning for Storm Surge Forecasting
Oden Institute for Computational Engineering & Sciences University of Texas at Austin（奥登计算工程与科学研究所 乌得勒支大学）；Department of Civil & Environmental Engineering & Earth Sciences University of Notre Dame（土木与环境工程及地球科学系 纽约大学）；Department of Data Science Norwegian University of Life Sciences（数据科学系 挪威生命科学大学）；Department of Numerical Analysis and Scientific Computing Simula Research Laboratory（数值分析与科学计算系 Simula 研究实验室）
Integrating Spatiotemporal Features in LSTM for Spatially Informed COVID-19 Hospitalization Forecasting
Heilbrunn Department of Population and Family Health, Columbia University Mailman School of Public Health（哥伦比亚大学流行病学与家庭健康系）；Department of Geography, University of Colorado（科罗拉多大学地理系）；Population Council（人口理事会）
Center for Computation and Technology（计算技术中心）；Louisiana State University（路易斯安那州立大学）；Department of Physics（物理系）；Aristotle University of Thessaloniki（塞萨洛尼基阿利克塞奥斯大学）；Oden Institute for Computational Engineering and Sciences（计算工程与科学研究院）；The University of Texas at Austin（德克萨斯大学奥斯汀分校）
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）
A Privacy-Preserving Federated Framework with Hybrid Quantum-Enhanced Learning for Financial Fraud Detection
University of Melbourne（墨尔本大学）；Trinity College Dublin（都柏林三一学院）；Indian Institute of Technology（印度理工学院）；Birla Institute of Technology and Science Pilani（比拉理工学院和科学学院）；South Asian University（南亚大学）；New York University Abu Dhabi (NYUAD)（纽约大学阿布扎克分校）；NYUAD Research Institute（NYUAD研究机构）
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）
GTS_Forecaster: a novel deep learning based geodetic time series forecasting toolbox with python
School of Transportation Engineering, East China Jiao Tong University（东华交通大学交通运输工程学院）；Jiangxi Provincial Key Laboratory of Water Ecological Conservation in Headwater Regions（江西省头水地区水生态保育重点实验室）；Division of Geodetic Science, School of Earth Sciences, The Ohio State University（地球科学学院地质科学系，俄亥俄州立大学）；Institute Dom Luiz, University of Beira Interior（贝拉内里亚大学多姆路易斯研究所）；Institute of Meteorology and Climatology, Leibniz University Hannover（汉诺威莱比锡大学气象与气候研究所）；Key Laboratory of Poyang Lake Wetland and Watershed Research, Ministry of Education, Jiangxi Normal University（江西省鄱阳湖湿地与流域研究重点实验室，江西省教育厅，江西师范大学）
HURRI-GAN: A Novel Approach for Hurricane Bias-Correction Beyond Gauge Stations using Generative Adversarial Networks
Center of Computation and Technology, Louisiana State University（路易斯安那州立大学计算与技术中心）；Saint-Joseph University of Beirut（贝鲁特圣约瑟夫大学）；Department of Physics, Aristotle University of Thessaloniki（塞萨洛尼基阿列克斯大学物理系）；Center for Interdisciplinary Research and Innovation, Aristotle University of Thessaloniki（塞萨洛尼基阿列克斯大学跨学科研究中心）；Department of Computer Science, Louisiana State University（路易斯安那州立大学计算机科学系）；Oden Institute for Computational Engineering and Sciences, The University of Texas at Austin（德克萨斯大学奥斯汀分校奥登计算工程与科学研究院）
21cmEMUv3: a hybrid diffusion-LSTM emulator of 21cmFAST summary observables
Research Center for the Early Universe, Graduate School of Science, The University of Tokyo（早期宇宙研究中心，东京大学研究生院）；Department of Physics, Graduate School of Science, The University of Tokyo（东京大学研究生院物理系）；Scuola Normale Superiore (SNS), Piazza dei Cavalieri 7, Pisa（普拉蒂亚学院（SNS），比萨）；Physics Department, Stellenbosch University（斯坦福博斯奇大学物理系）；Cosmic Dawn Center (DAWN)（黎明宇宙中心（DAWN））；Niels Bohr Institute, University of Copenhagen（哥本哈根大学尼尔斯·波尔研究所）；SISSA, Via Bonomea 265, 34136 Trieste（SISSA，特里斯特）；INFN Sezione di Trieste（INFN特里埃斯特分部）；Centro Nazionale di Ricerca in High Performance Computing, Big Data e Quantum Computing（高性能计算、大数据和量子计算国家研究中心）；Physics Department, Blackett Lab, Imperial College London（伦敦帝国理工学院布莱特实验室物理系）
