Mean aortic pressure (MAP) is a major determinant of perfusion in all organs systems. The ability to forecast MAP would enhance the ability of physicians to estimate prognosis of the patient and assist in early detection of hemodynamic instability. However, forecasting MAP is challenging because the blood pressure (BP) time series is noisy and can be highly non-stationary. The aim of this study was to forecast the mean aortic pressure five minutes in advance, using the 25 Hz time series data of previous five minutes as input. We provide a benchmark study of different deep learning models for BP forecasting. We investigate a left ventricular dwelling transvalvular micro-axial device, the Impella, in patients undergoing high-risk percutaneous intervention. The Impella provides hemodynamic support, thus aiding in native heart function recovery. It is also equipped with pressure sensors to capture high frequency MAP measurements at origin, instead of peripherally. Our dataset and the clinical application is novel in the BP forecasting field. We performed a comprehensive study on time series with increasing, decreasing, and stationary trends. The experiments show that recurrent neural networks with Legendre Memory Unit achieve the best performance with an overall forecasting error of 1.8 mmHg.
Probabilistic Forecasting of Electric Vehicle Charging in a Time-varying Hierarchical Infrastructure
Laboratoire de Mathematiques d’Orsay, Université Paris-Saclay, CNRS, INRIA（奥赛数学实验室，巴黎萨克雷大学，法国国家科学研究中心，法国国家信息与自动化研究所）；Université Paris-Cité（巴黎西岱大学）；Electricité de France R&D（法国电力公司研发部）
Benchmarking Physics-Informed Time-Series Models for Operational Global Station Weather Forecasting
Department of Computer Science and Engineering, Hong Kong University of Science and Technology, Hong Kong SAR China（香港科技大学计算机科学与工程系）；Department of Computer Science and Engineering, Southern University of Science and Technology, Shenzhen, China（南方科技大学计算机科学与工程系）；School of Computer and Information Sciences, University of Newcastle, Newcastle, Australia（新castle大学计算机与信息科学学院）；Hangzhou Innovation Institute of Beihang University, Hangzhou, China（北京航空航天大学杭州创新研究院）；Shanghai Artificial Intelligence Laboratory, Shanghai, China（上海人工智能实验室）
Innovation ARIMA models application to predict pressure variations in water supply networks with open-loop control. Case study in Noja (Cantabria, Spain)
Forecasting MBTA Transit Dynamics: A Performance Benchmarking of Statistical and Machine Learning Models
Sunset High School（Sunset 高中）；Woodbridge High School（Woodbridge 高中）；Western Academy of Beijing（北京西方学院）；Computer Science Department, Metropolitan College（Metropolitan College 计算机科学系）
Deep Learning-Accelerated 3D Carbon Storage Reservoir Pressure Forecasting Based on Data Assimilation Using Surface Displacement from InSAR
Lawrence Livermore National Laboratory（劳伦斯利弗莫尔国家实验室）；TotalEnergies E&P Research and Technology（道达尔能源勘探与生产研究技术中心）；University of North Dakota, Energy & Environmental Research Center（北达科他大学能源与环境研究中心）
通过同化监测数据快速预测地质碳封存储层压力分布是一个具有挑战性的问题。由于高钻井成本，GCS项目通常具有来自井的空间稀疏测量，导致储层压力预测的高不确定性。为了应对这一挑战，我们建议使用低成本的干涉合成孔径雷达（InSAR）数据作为监测数据来推断储层压力的建立。我们开发了一个深度学习加速的工作流程，以同化从InSAR解释的地表位移图，并预测动态储层压力。该工作流程采用Encourage Smoother Multiple Data Assimilation（ES-MDA）框架，更新三维（3D）地质属性，并预测具有量化不确定性的储层压力。我们使用一个合成的商业规模的GCS模型双峰分布的渗透率和孔隙度，以证明工作流程的有效性。一个两步CNN-PCA方法被用来参数化的双峰字段。工作流程的计算效率通过两个基于剩余U-Net的代理模型分别用于地表位移和储层压力预测来提高。该工作流程可在个人计算机上在半小时内完成数据同化和储层压力预测。
Generative Spatio-temporal GraphNet for Transonic Wing Pressure Distribution Forecasting
Faculty of Engineering and Physical Sciences, University of Southampton（南安普顿大学工程与物理科学学院）；School of Engineering, Zurich University of Applied Sciences ZHAW（苏黎世应用科技大学工程学院）；Federal Institute of Technology Zurich ETHZ（苏黎世联邦理工学院）
A Transformer-based Diffusion Probabilistic Model for Heart Rate and Blood Pressure Forecasting in Intensive Care Unit
The University of Arizona（亚利桑那大学）；Zhongshan Hospital, Fudan University（复旦大学附属中山医院）；The Shanghai Institute of Cardiovascular Diseases（上海市心血管病研究所）；Bio5 Institute, The University of Arizona（亚利桑那大学生物5研究所）；College of Nursing, The University of Arizona（亚利桑那大学护理学院）
A Deep Learning-Accelerated Data Assimilation and Forecasting Workflow for Commercial-Scale Geologic Carbon Storage
Lawrence Livermore National Laboratory（劳伦斯利弗莫尔国家实验室）；Total E&P Research and Technology（道达尔勘探与生产研究和技术公司）；Energy & Environmental Research Center, University of North Dakota（北达科他大学能源与环境研究中心）
Wind Power Forecasting Considering Data Privacy Protection: A Federated Deep Reinforcement Learning Approach
Northeast Electric Power University（东北电力大学）；State Grid Jilin Electric Power Co., Ltd. Jilin Power Supply Company（国网吉林省电力有限公司吉林供电公司）；Huazhong University of Science and Technology（华中科技大学）；Naval University of Engineering（海军工程大学）；Cranfield University（克兰菲尔德大学）
DeepKoopFormer: A Koopman Enhanced Transformer Based Architecture for Time Series Forecasting
Helmholtz Center for Environmental Research-UFZ（海德堡环境研究中心-UFZ）；Delft Center for Systems and Control（代尔夫特系统与控制中心）；School of Engineering, University of Pittsburgh（匹兹堡大学工程学院）
Tropical Cyclone Track Forecasting using Fused Deep Learning from Aligned Reanalysis Data
University of Colorado（科罗拉多大学）；Univ. Grenoble Alpes（格勒诺布尔阿尔卑斯大学）；Univ. Savoie Mont Blanc（萨瓦大学）；CNRS（法国国家科学研究中心）；IRD（法国发展研究院）；IFSTTAR（法国交通发展规划与网络研究院）；ISTerre（地球科学研究所）；Linear Accelerator Laboratory（线性加速器实验室）；Université Paris-Sud（巴黎第十一大学）；Inria Saclay–Ile-de-France（法国国家信息与自动化研究所萨克莱-法兰西岛分院）；LRI（信息学实验室）；University of Colorado Boulder（科罗拉多大学博尔德分校）；Cooperative Institute for Research in Environmental Sciences（环境科学合作研究所）；NOAA/OAR/ESRL/Global Systems Division（美国国家海洋和大气管理局海洋与大气研究办公室地球系统研究实验室全球系统部）
A Climate-Aware Deep Learning Framework for Generalizable Epidemic Forecasting
School of Engineering（工程学院）；Brown University（布朗大学）；Department of Epidemiology（流行病学系）；School of Public Health（公共卫生学院）；Institute at Brown for Environment and Society（布朗大学环境与社会研究所）
Salt-Rock Creep Deformation Forecasting Using Deep Neural Networks and Analytical Models for Subsurface Energy Storage Applications
IIT (ISM) Dhanbad（印度理工学院（印度矿业学院）丹巴德分校）；Sorbonne University, Abu Dhabi（索邦大学阿布扎比校区）；Sorbonne Center for Artificial Intelligence, Sorbonne University（索邦大学人工智能中心）；CSIRO Energy（澳大利亚联邦科学与工业研究组织能源研究所）
Differentiable latent structure discovery for interpretable forecasting in clinical time series
Université Paris Cité and Université Sorbonne Paris Nord, INRIA, Inserm, HEKA（巴黎大学和巴黎-索邦大学、INRIA、Inserm、HEKA）；Department of Medical Informatics, Assistance Publique Hôpitaux de Paris (AP-HP), Georges Pompidou European Hospital（医学信息学部，巴黎公共医院（AP-HP），乔治·蓬皮杜欧洲医院）；SIERRA, Inria Paris（SIERRA，Inria巴黎）；Département d’Informatique de l’École Normale Supérieure（高等师范学院计算机系）；PSL Research University（PSL研究大学）
