Forecasting Using Reservoir Computing: The Role of Generalized Synchronization
University of California San Diego（加州大学圣迭戈分校）；Cooperative Institute for Research in Environmental Sciences at the University of Colorado Boulder（科罗拉多大学博尔德分校环境科学合作研究所）；NOAA Physical Sciences Laboratory（美国国家海洋和大气管理局物理科学实验室）；Scripps Institution of Oceanography, University of California San Diego（加州大学圣迭戈分校斯克里普斯海洋研究所）
Jason A. Platt, Adrian Wong, Randall Clark, Stephen G. Penny, Henry D. I. Abarbanel
备注：
This is the Shortened Version of the Paper, the longer paper, Robust Forecasting through Generalized Synchronization in Reservoir Computing, can be found at arXiv:2103.00362
Reservoir computers (RC) are a form of recurrent neural network (RNN) used for forecasting time series data. As with all RNNs, selecting the hyperparameters presents a challenge when training on new inputs. We present a method based on generalized synchronization (GS) that gives direction in designing and evaluating the architecture and hyperparameters of a RC. The 'auxiliary method' for detecting GS provides a pre-training test that guides hyperparameter selection. Furthermore, we provide a metric for a "well trained" RC using the reproduction of the input system's Lyapunov exponents.
Cross-Scale Reservoir Computing for large spatio-temporal forecasting and modeling
Research Center ``Enrico Fermi'', Rome, Italy（恩里科·费米研究中心）；National Center for Radiation Protection（辐射防护国家中心）；Computational Physics, Italian National Institute of Health, Rome, Italy（计算物理，意大利卫生研究院）；Department of Physics, ``Tor Vergata'' University, Rome, Italy（物理系，托尔维加塔大学）；Department of Civil, Computer Science（土木工程系）；Aeronautical Technologies Engineering, ``Roma Tre'' University, Rome, Italy（航空技术工程，罗马三大学）
Introducing Randomized High Order Fuzzy Cognitive Maps as Reservoir Computing Models: A Case Study in Solar Energy and Load Forecasting
Federal University of Minas Gerais（米纳斯吉拉斯联邦大学）；Federal Institute of Education Science and Technology of Northern Minas Gerais（北米纳斯吉拉斯联邦教育科技学院）；Universidade Federal de Minas Gerais（米纳斯吉拉斯联邦大学）
Institute of Fundamental and Frontier Sciences, University of Electronic Science and Technology of China（电子科技大学基础与前沿科学研究所）；Potsdam Institute for Climate Impact Research（波茨坦气候影响研究所）；Department of Physics, Humboldt University Berlin（柏林洪堡大学物理系）；Research Institute of Intelligent Complex Systems, Fudan University（复旦大学智能复杂系统研究所）；School of Physics, University of Electronic Science and Technology of China（电子科技大学物理学院）；Key Laboratory of Quantum Physics and Photonic Quantum Information, Ministry of Education, University of Electronic Science and Technology of China（电子科技大学教育部量子物理与光子量子信息重点实验室）；Non-classical Information Science Basic Discipline Research Center of Sichuan Province, University of Electronic Science and Technology of China（电子科技大学四川省非经典信息科学基础学科研究中心）
The Role of Entanglement in Quantum Reservoir Computing with Coupled Kerr Nonlinear Oscillators
Department of Physics and Astronomy, University of Calgary（卡尔加里大学物理与天文学系）；Institute for Quantum Science and Technology, University of Calgary（卡尔加里大学量子科学与技术研究所）；Hotchkiss Brain Institute, University of Calgary（卡尔加里大学霍奇基斯脑研究所）；Department of Physics, University of New Brunswick（新不伦瑞克大学物理系）
Deep Time-Delay Reservoir Computing: Dynamics and Memory Capacity
Institute of Theoretical Physics, Technische Universität Berlin（柏林工业大学理论物理研究所）；Department of Mathematics, Humboldt-Universität zu Berlin（柏林洪堡大学数学系）；Institute of Mathematics, Technische Universität Berlin（柏林工业大学数学研究所）
Airbus Central Research & Technology（空客中央研究与技术中心）；Department of Computer Science（计算机科学系）；University of Pisa, Italy（意大利比萨大学）；Bielefeld University, Germany（德国比勒菲尔德大学）
Inertial Confinement Fusion Forecasting via Large Language Models
Rochester Institute of Technology（罗切斯特理工学院）；University of Rochester（罗切斯特大学）；Meta AI；University of California, Los Angeles（加利福尼亚大学洛杉矶分校）；Pacific Northwest National Laboratory（太平洋西北国家实验室）
Hybridizing Traditional and Next-Generation Reservoir Computing to Accurately and Efficiently Forecast Dynamical Systems
University of Maryland（马里兰大学）；University of Chicago（芝加哥大学）；Institute for Research in Electronics and Applied Physics (IREAP)（电子与应用物理研究所）；Institute for Physical Science and Technology (IPST)（物理科学与技术研究所）；Santa Fe Institute（圣达菲研究所）
Modeling Nonlinear Oscillator Networks Using Physics-Informed Hybrid Reservoir Computing
University of Bristol, School of Computer Science（布里斯托大学计算机科学学院）；University of Bristol, School of Engineering Mathematics and Technology（布里斯托大学工程数学与技术学院）
Data-Specific Hyper-Parameter Design: A Paradigm Shift in Reservoir Computing
University of Pretoria. Department of Mathematics and Applied Mathematics（南非大学 Pretoria分校 数学与应用数学系）；Nanyang Technological University. Division of Mathematical Sciences. School of Physical and Mathematical Sciences（南洋理工大学 数学科学学院 物理与数学科学学院）
Recurrent Quantum Feature Maps for Reservoir Computing
Department of Physics, University of Ottawa（渥太华大学物理系）；National Research Council of Canada（加拿大国家研究委员会）；Institute for Quantum Science and Technology, Department of Physics and Astronomy, University of Calgary（卡尔加里大学物理与天文学系量子科学与技术研究所）；Hotchkiss Brain Institute, University of Calgary（卡尔加里大学霍奇基斯脑研究所）
