The remarkable flexibility and adaptability of both deep learning models and ensemble methods have led to the proliferation for their application in understanding many physical phenomena. Traditionally, these two techniques have largely been treated as independent methodologies in practical applications. This study develops an optimized ensemble deep learning (OEDL) framework wherein these two machine learning techniques are jointly used to achieve synergistic improvements in model accuracy, stability, scalability, and reproducibility prompting a new wave of applications in the forecasting of dynamics. Unpredictability is considered as one of the key features of chaotic dynamics, so forecasting such dynamics of nonlinear systems is a relevant issue in the scientific community. It becomes more challenging when the prediction of extreme events is the focus issue for us. In this circumstance, the proposed OEDL model based on a best convex combination of feed-forward neural networks, reservoir computing, and long short-term memory can play a key role in advancing predictions of dynamics consisting of extreme events. The combined framework can generate the best out-of-sample performance than the individual deep learners and standard ensemble framework for both numerically simulated and real world data sets. We exhibit the outstanding performance of the OEDL framework for forecasting extreme events generated from Lienard-type system, prediction of COVID-19 cases in Brazil, dengue cases in San Juan, and sea surface temperature in Nino 3.4 region.
A Hybrid Autoencoder-Transformer Model for Robust Day-Ahead Electricity Price Forecasting under Extreme Conditions
Beijing Normal-Hong Kong Baptist University（北京师范大学-香港 Baptist大学）；Shenzhen Research Institute of Big Data（深圳大数据研究院）；School of Science and Engineering（科学与工程学院）；School of Electrical and Power Engineering（电气与电力工程学院）
TSSM: Triaxial State Space Model for Global Station Weather Forecasting with Temporal-Variable-Historical Modeling
Beihang University（北京航空航天大学）；Shanghai Artificial Intelligence Laboratory（上海人工智能实验室）；The Chinese University of Hong Kong（香港中文大学）；Tsinghua University（清华大学）
R$^2$Energy: A Large-Scale Benchmark for Robust Renewable Energy Forecasting under Diverse and Extreme Conditions
Center for Urban Science and Computation, Tsinghua University（城市科学与计算中心，清华大学）；Courant Institute of Mathematical Sciences, New York University（数学科学学院，纽约大学）
Optimized Dynamic Mode Decomposition for Reconstruction and Forecasting of Atmospheric Chemistry Data
University of Washington（华盛顿大学）；Morgan State University（摩根州立大学）；NASA Global Modelling and Assimilation Office, Goddard Space Flight Center（美国国家航空航天局戈达德航天中心全球建模与同化办公室）
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）
Time series forecasting based on optimized LLM for fault prediction in distribution power grid insulators
Center of Technology and Systems (UNINOVA-CTS)（技术与系统中心（UNINOVA-CTS））；Associated Lab of Intelligent Systems (LASI)（智能系统联合实验室（LASI））；Faculty of Engineering and Applied Sciences, University of Regina（里贾纳大学工程与应用科学学院）
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）
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）
A Synergistic Approach: Dynamics-AI Ensemble in Tropical Cyclone Forecasting
Institute of Atmospheric Physics, Chinese Academy of Sciences（中国科学院大气物理研究所）；University of Chinese Academy of Sciences（中国科学院大学）；Shanghai Academy of Artificial Intelligence for Science（上海科学智能研究院）；Fudan University（复旦大学）；Henan Meteorological Observatory（河南省气象台）；Tianjin Meteorological Information Center（天津市气象信息中心）
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 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）
