Extreme events in society and nature, such as pandemic spikes, rogue waves, or structural failures, can have catastrophic consequences. Characterizing extremes is difficult as they occur rarely, arise from seemingly benign conditions, and belong to complex and often unknown infinite-dimensional systems. Such challenges render attempts at characterizing them as moot. We address each of these difficulties by combining novel training schemes in Bayesian experimental design (BED) with an ensemble of deep neural operators (DNOs). This model-agnostic framework pairs a BED scheme that actively selects data for quantifying extreme events with an ensemble of DNOs that approximate infinite-dimensional nonlinear operators. We find that not only does this framework clearly beat Gaussian processes (GPs) but that 1) shallow ensembles of just two members perform best; 2) extremes are uncovered regardless of the state of initial data (i.e. with or without extremes); 3) our method eliminates "double-descent" phenomena; 4) the use of batches of suboptimal acquisition points compared to step-by-step global optima does not hinder BED performance; and 5) Monte Carlo acquisition outperforms standard optimizers in high-dimensions. Together these conclusions form the foundation of an AI-assisted experimental infrastructure that can efficiently infer and pinpoint critical situations across many domains, from physical to societal systems.
Discovering Spatial Correlations of Earth Observations for weather forecasting by using Graph Structure Learning
Data Assimilation Group\ Institute of Atmospheric Prediction Systems 35, 5-gil, Boramae-ro Seoul Republic of Korea 07071；Network Science Lab\ Catholic University of Korea 43 Jibong-ro Bucheon Republic of Korea 14662；Data Assimilation Group\ Institute of Atmospheric Prediction Systems；Network Science Lab\ Catholic University of Korea
RealBench: Benchmarking Data-Driven Numerical Weather Forecasting Under Operational Conditions and Extreme Event Challenges
The Hong Kong University of Science and Technology（香港科学与技术大学）；Nanjing University（南京大学）；Southern University of Science and Technology（南方科技大学）；Shanghai AI Laboratory（上海人工智能实验室）；Shanghai TechWind Technology Co., Ltd.（上海技风科技有限公司）
Enhancing the Forecasting Capability of Multi-Model Blending Algorithms for Extreme Precipitation via Joint Use of Station and Gridded Observations
National Meteorological Center of China Meteorological Administration（中国气象局国家气象中心）；Xiong’an Institute of Meteorological Artificial Intelligence（雄安气象人工智能研究所）；China Meteorological Administration Training Center（中国气象局培训中心）
Frequency-aware forecasting for short-term typhoon gust prediction
School of Artificial Intelligence, Hubei University（湖北大学人工智能学院）；School of Economics and Management, Wuhan University（武汉大学经济与管理学院）；School of Electrical Engineering, Shanghai Jiao Tong University（上海交通大学电气工程学院）
Explainable Earth Surface Forecasting under Extreme Events
Image Processing Laboratory (IPL), Universitat de València（瓦伦西亚大学图像处理实验室）；Remote Sensing Centre for Earth System Research (RSC4Earth), Leipzig University（莱比锡大学地球系统研究遥感中心）；Institute for Earth System Science and Remote Sensing, Leipzig University（莱比锡大学地球系统科学与遥感研究所）；Image and Signal Processing Group, Leipzig University（莱比锡大学图像与信号处理组）；Helmholtz Centre for Environmental Research – UFZ（亥姆霍兹环境研究中心）
Enhancing Tropical Cyclone Path Forecasting with an Improved Transformer Network
Faculty of Electronics and Telecommunications University of Engineering and Technology, VNU Hanoi, Vietnam（电子电信学院 工程技术大学 巴黎越南河内分校）；Faculty of Information Technology University of Engineering and Technology, VNU Hanoi, Vietnam（信息技术学院 工程技术大学 巴黎越南河内分校）
Interpretable Physics-Informed Load Forecasting for U.S. Grid Resilience: SHAP-Guided Ensemble Validation in Hybrid Deep Learning Under Extreme Weather
1Dept. of Computer Science, Midwestern State University, Dallas, TX, USA 2O\&M Analytics, AES Clean Energy, The AES Corporation, Louisville, CO, USA 3Dept. of Information；Communication Engineering, Pabna University of Science
Synergistic Neural Forecasting of Air Pollution with Stochastic Sampling
Mohamed bin Zayed University of Artificial Intelligence（莫德尔·本·扎耶德人工智能大学）；Weizmann Institute of Science（魏兹曼科学研究所）；Linköping University（林雪平大学）；Australian National University（澳大利亚国立大学）
Generating Fine-Grained Causality in Climate Time Series Data for Forecasting and Anomaly Detection
University of Illinois Urbana-Champaign（伊利诺伊大学厄巴纳-香槟分校）；IBM Research（IBM研究院）；MIT-IBM Watson AI Lab（麻省理工学院-IBM沃森人工智能实验室）；Amazon Sustainability Science and Innovation（亚马逊可持续性科学与创新部门）
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）
FreqLens: Interpretable Frequency Attribution for Time Series Forecasting
Independent Researcher（独立研究者）；Indiana University at Bloomington（印第安纳大学布卢明顿分校）；National Yang Ming Chiao Tung University；National Taiwan University（国立台湾大学）；Boise State University（博伊州立大学）
Advancing Subseasonal Forecasting with Machine Learning
Harvard College（哈佛学院）；University of Toronto（多伦多大学）；Instituto de Matemática Pura e Aplicada（数学与应用数学研究所）；Massachusetts Institute of Technology（麻省理工学院）；Atmospheric and Environmental Research（大气与环境研究）；Microsoft Corporation（微软公司）；Rhiza Research（Rhiza研究）；Microsoft Research New England（微软新英格兰研究院）；European Centre for Medium-Range Weather Forecasts（欧洲中期天气预报中心）
