In this research paper, we study the capability of artificial neural network models to emulate storm surge based on the storm track/size/intensity history, leveraging a database of synthetic storm simulations. Traditionally, Computational Fluid Dynamics solvers are employed to numerically solve the storm surge governing equations that are Partial Differential Equations and are generally very costly to simulate. This study presents a neural network model that can predict storm surge, informed by a database of synthetic storm simulations. This model can serve as a fast and affordable emulator for the very expensive CFD solvers. The neural network model is trained with the storm track parameters used to drive the CFD solvers, and the output of the model is the time-series evolution of the predicted storm surge across multiple nodes within the spatial domain of interest. Once the model is trained, it can be deployed for further predictions based on new storm track inputs. The developed neural network model is a time-series model, a Long short-term memory, a variation of Recurrent Neural Network, which is enriched with Convolutional Neural Networks. The convolutional neural network is employed to capture the correlation of data spatially. Therefore, the temporal and spatial correlations of data are captured by the combination of the mentioned models, the ConvLSTM model. As the problem is a sequence to sequence time-series problem, an encoder-decoder ConvLSTM model is designed. Some other techniques in the process of model training are also employed to enrich the model performance. The results show the proposed convolutional recurrent neural network outperforms the Gaussian Process implementation for the examined synthetic storm database.
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）
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）
Enhancing Video Understanding: Deep Neural Networks for Spatiotemporal Analysis
School of Electrical and Computer Engineering, College of Engineering, University of Tehran（德黑兰大学工程学院电气与计算机工程学院）；School of Cognitive Sciences, Institute for Research in Fundamental Sciences (IPM)（基础科学研究所认知科学学院）
GLSFormer: Gated - Long, Short Sequence Transformer for Step Recognition in Surgical Videos
Johns Hopkins University（约翰斯·霍普金斯大学）；Wilmer Eye Institute, Johns Hopkins University School of Medicine（约翰斯·霍普金斯大学医学院威尔默眼科研究所）；Malone Center for Engineering in Healthcare, Johns Hopkins University（约翰斯·霍普金斯大学马龙医疗工程中心）
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）
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）
