It is proposed to use analogs of the forecast mean to generate an ensemble of perturbations for use in ensemble optimal interpolation (EnOI) or ensemble variational (EnVar) methods. A new method of constructing analogs using variational autoencoders (VAEs; a machine learning method) is proposed. The resulting analog methods using analogs from a catalog (AnEnOI), and using constructed analogs (cAnEnOI), are tested in the context of a multiscale Lorenz-`96 model, with standard EnOI and an ensemble square root filter for comparison. The use of analogs from a modestly-sized catalog is shown to improve the performance of EnOI, with limited marginal improvements resulting from increases in the catalog size. The method using constructed analogs (cAnEnOI) is found to perform as well as a full ensemble square root filter, and to be robust over a wide range of tuning parameters.
Enhancing the Parameterization of Reservoir Properties for Data Assimilation Using Deep VAE-GAN
Departamento de Engenharia de Minas e de Petróleo, Escola Politécnica, Universidade de São Paulo（圣保罗大学采矿与石油工程系，理工学院）；Department of Earth Science and Engineering, Imperial College London（伦敦帝国理工学院地球科学与工程系）
DAWP: A framework for global observation forecasting via Data Assimilation and Weather Prediction in satellite observation space
Shanghai Jiao Tong University（上海交通大学）；Fudan University（复旦大学）；The Chinese University of Hong Kong Shanghai AI Laboratory（香港中文大学上海人工智能实验室）；Shanghai AI Laboratory（上海人工智能实验室）
Reconstructing Multi-Scale Physical Fields from Extremely Sparse Measurements with an Autoencoder-Diffusion Cascade
Internet of Things Thrust（物联网方向）；Intelligent Transportation Thrust（智能交通方向）；Marine Hydrodynamic Research Facility（海洋流体研究设施）；Department of Civil and Environmental Engineering（土木与环境工程系）
CktGen: Automated Analog Circuit Design with Generative Artificial Intelligence
College of Computer Science and Technology（计算机科学与技术学院）；Australian Artificial Intelligence Institute（澳大利亚人工智能研究所）；School of Aeronautics and Astronautics（航空宇航科学学院）；School of Computing（计算科学学院）
Compressing high-resolution data through latent representation encoding for downscaling large-scale AI weather forecast model
Shanghai Academy of Artificial Intelligence for Science（上海科学人工智能研究院）；Shanghai Normal University（上海师范大学）；Jiangsu Meteorological Observatory（江苏省气象台）；Artificial Intelligence Innovation and Incubation Institute, Fudan University（复旦大学人工智能创新与孵化研究院）；Nanjing Meteorological Observatory（南京市气象台）
Shandong Provincial Key Laboratory of Network Based Intelligent Computing（山东省网络智能计算重点实验室）；University of Jinan（济南大学）；Jinan, 250022, China（济南市，250022，中国）
Learning Minimal Representations of Many-Body Physics from Snapshots of a Quantum Simulator
1 Institute of Science；Technology Austria, Am Campus 1, 3400 Klosterneuburg, Austria 2 Vienna Center for Quantum Science；Technology (VCQ), Atominstitut, TU Wien, Vienna, Austria 3 ICFO – Institut de Ci\`encies Fot\`oniques, The Barcelona Institute of Science；Technology, Av. Carl Friedrich Gauss 3, 08860 Castelldefels (Barcelona), Spain 4 University of Innsbruck, Department for Theoretical Physics, Technikerstr. 21a, A-6020 Innsbruck, Austria
