California Institute of Technology（加州理工学院）

EOS-Bench: A Comprehensive Benchmark for Earth Observation Satellite Scheduling

EOS-Bench：一个全面的地球观测卫星调度基准

Qian Yin, Jiaxing Li, Jiaqi Cheng, Qizhang Luo, Annalisa Riccardi, Abhijit Chatterjee, Rafael Vazquez, Carlo Novara, Michalis Mavrovouniotis, Ponnuthurai Nagaratnam Suganthan, Shengzhou Bai, Xiaoxuan Hu, Lining Xing, Ming Xu, Shuang Li, Zixuan Zheng, Xin Shen, Xiaoyu Chen, Yi Gu, Yanjie Song, Witold Pedrycz, Evan L. Kramer, Laio Oriel Seman, Cletah Shoko, Guohua Wu, Xinwei Wang

机构 * School of Traffic ； Transportation Engineering, Central South University, Changsha 410083, China ； School of Engineering ； Materials Science, Queen Mary University of London, London E1 4NS, UK ； College of Automation, Central South University, Changsha, 410083, China ； Aerospace Engineering, University of Strathclyde, Glasgow G1 1XQ, UK ； Department of Computer Science, University of Exeter, Exeter EX4 4QJ, UK ； Department of Aerospace Engineering, Universidad de Sevilla, Camino de los Descubrimientos s.n., Sevilla, 41092, Spain ； Department of Electronics ； ERATOSTHENES Centre of Excellence, Limassol, 3012, Cyprus ； Department of Civil Engineering ； Geomatics, Cyprus University of Technology, Limassol, 3036, Cyprus ； Department of Computer Science ； Engineering, College of Engineering, Qatar University, Doha, 2713, Qatar ； Department of Aerospace Engineering, Korea Advanced Institute of Science ； School of Management, Hefei University of Technology, Hefei, 230009, China ； Key Laboratory of Collaborative Intelligence Systems, Ministry of Education, Xidian University, Xi’an 710071, China ； School of Astronautics, Beihang University, 102206 Beijing, China ； Advanced Space Technology Laboratory, College of Astronautics, Nanjing University of Aeronautics ； National Key Laboratory of Aerospace Flight Dynamics, Northwestern Polytechnical University, Xi’an, 710072, China ； State Key Laboratory of Information Engineering in Surveying, Mapping ； Remote Sensing, Wuhan University, Wuhan, 430079, China ； School of Computer Science, China University of Geosciences, Wuhan, 430074, China ； School of Information Science ； Technology, Dalian Maritime University, Dalian, 116026, China ； Department of Electrical \& Computer Engineering, University of Alberta, Edmonton, AB T6R 2V4, Canada ； Planetary Science, California Institute of Technology, CA, USA ； Department of Automation ； Systems Engineering, Federal University of Santa Catarina, Florianopolis, SC, Brazil ； School of Geography, Archaeology ； Environmental Studies, University of the Witwatersrand, Braamfontein, Johannesburg, South Africa 纠错

AI总结 本文提出EOS-Bench，通过整合高保真轨道动力学和平台约束，生成1390个场景和13900个基准实例，评估调度方法的系统性和可重复性，涵盖从小型验证案例到1000颗卫星和10000个请求的复杂问题。

Adaptive Meta-Learning Stochastic Gradient Hamiltonian Monte Carlo Simulation for Bayesian Updating of Structural Dynamic Models

自适应元学习随机梯度Hamilton-Monte Carlo模拟用于结构动态模型的贝叶斯更新

Xianghao Meng, James L. Beck, Yong Huang, Hui Li

机构 * Key Lab of Smart Prevention and Mitigation of Civil Engineering Disasters of the Ministry of Industry and Information Technology, Harbin Institute of Technology, Harbin, China（工信部智能防灾减灾重点实验室，哈尔滨工业大学，哈尔滨，中国） ； Key Lab of Structures Dynamic Behavior and Control of the Ministry of Education, Harbin Institute of Technology, Harbin, China（教育部结构动力行为与控制重点实验室，哈尔滨工业大学，哈尔滨，中国） ； Division of Engineering and Applied Science, California Institute of Technology, CA, USA（加州理工学院工程与应用科学系，CA，美国） 纠错

AI总结 本文提出一种自适应元学习随机梯度Hamilton-Monte Carlo算法，通过训练适应性神经网络优化采样策略，实现无需进一步训练即可应用于同类结构贝叶斯更新问题，提升效率与通用性。

Journal ref Comput Meth Appl Mech Eng; 437: 117753 (2025)

Dictionary learning for Kernel EDMD

基于核的扩展动态模式分解的字典学习

Erik Lien Bolager, Boumediene Hamzi, Houman Owhadi, Ioannis G. Kevrekidis, Felix Dietrich

机构 * School of Computation, Information and Technology, Munich Data Science Institute, Munich Center for Machine Learning, Technical University of Munich, Germany（慕尼黑计算、信息与技术学院，慕尼黑数据科学研究所，慕尼黑机器学习中心，技术大学慕尼黑，德国） ； Department of Computing and Mathematical Sciences, Caltech, USA（计算与数学科学部，加州理工学院，美国） ； The Alan Turing Institute, UK（艾伦·图灵研究所，英国） ； Departments of Chemical and Biomolecular Engineering and of Applied Mathematics and Statistics, Johns Hopkins University, USA（化学与生物分子工程系和应用数学与统计学系，约翰霍普金斯大学，美国） 纠错

AI总结 本文提出通过字典学习优化核参数，改进核扩展动态模式分解以更高效逼近Koopman算子，通过去除不重要核函数提升性能。