The Two-Hump Problem: Bridging the Difficulty Gap in Mathematical Reinforcement Learning
双峰问题：弥合数学强化学习中的难度差距
Lucas Fagan, Michele Tarquini, Ali Shehper, Maksymilian Manko, Angus Gruen, Coco Huang, Giorgi Butbaia, Davide Passaro, Sergei Gukov
机构
*
Department of Mathematics, California Institute of Technology（加州理工学院数学系）
；
Institute of Mathematics, University of Zurich（苏黎世大学数学研究所）
；
Zero Latency Labs（零延迟实验室）
；
Department of Mathematics, Temple University（天普大学数学系）
CommentsAccepted to the 2026 IEEE International Conference on Robotics and Automation (ICRA 2026). Copyright transferred to IEEE. Sample code for the navigation example with CBF-RL reward core construction can be found at https://github.com/lzyang2000/cbf-rl-navigation-demo
Flow Matching for Efficient and Scalable Data Assimilation
用于高效可扩展数据同化的流匹配
Taos Transue, Bohan Chen, So Takao, Bao Wang
机构
*
The Computing and Mathematical Sciences Department, California Institute of Technology（加州理工学院计算与数学科学系）
；
Department of Mathematics and Scientific Computing and Imaging Institute, University of Utah（犹他大学数学与科学计算系和成像研究所）
Gaussian process policy iteration with additive Schwarz acceleration for forward and inverse HJB and mean field game problems
基于高斯过程策略迭代与加性Schwarz加速的正向和逆向HJB及平均场博弈问题
Xianjin Yang, Jingguo Zhang
机构
*
Department of Computing and Mathematical Sciences, California Institute of Technology, CA, USA（计算与数学科学系，加州理工学院，CA，美国）
；
Department of Mathematics and Risk Management Institute, National University of Singapore, Singapore（数学与风险管理研究所，新加坡国立大学，新加坡）
SHIELD: Safety on Humanoids via CBFs In Expectation on Learned Dynamics
SHIELD: 基于学习动力学期望的控制障碍函数实现人形机器人安全
Lizhi Yang, Blake Werner, Ryan K. Cosner, David Fridovich-Keil, Preston Culbertson, Aaron D. Ames
机构
*
Mechanical and Civil Engineering, California Institute of Technology（加州理工学院机械与土木工程系）
；
Aerospace Engineering and Engineering Mechanics, UT Austin（德克萨斯大学奥斯汀分校航空航天工程与工程力学系）
；
Computer Science, Cornell University（康奈尔大学计算机科学系）
CommentsAccepted to the 2025 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS 2025). Copyright transferred to IEEE. Video at https://youtu.be/-Qv1wR4jfj4
Review of Machine Learning Models for Solar Energetic Particle Prediction
太阳高能粒子预测的机器学习模型综述
Spiridon Kasapis, Pouya Hosseinzadeh, Kathryn Whitman, Ricky Egeland, Manolis Georgoulis, Angelos Vourlidas, Athanasios Papaioannou, Eleni Lavasa, Anastasios Anastasiadis, Giorgos Giannopoulos, Andres Munoz-Jaramillo, Bala Poduval, Irina N. Kitiashvili, Alexander G. Kosovichev, Viacheslav Sadykov, Soukaina Filali Boubrahimi, Tate T. Hutchins, Hameedullah A. Farooki, Manuel E. Cuesta, Leng Y. Khoo, Sungmin Pak, Robert Czarnota, Jamie S. Rankin, Jamey Szalay, Mitchell M. Shen, Georgios Livadiotis, Zigong Xu, David J. McComas, Nikolaos Sarlis, Dionissios Hristopulos, Arik Posner, Alec J. Engell, Mohammed AbuBakr Ali, Ali G. A. Abdelkawy, Abdelrazek M. K. Shaltout, M. M. Beheary, Christina O. Lee, Sigiava Aminalragia-Giamini, Constantinos Papadimitriou, Ingmar Sandberg, Savvas Raptis, Shah Muhammad Hamdi, Monica Laurenza, Mirko Stumpo, Sumanth A. Rotti, India Jackson, Aatiya Ali, Atilim Gunes Baydin, Nathan Schwadron, Subhamoy Chatterjee, Maher A. Dayeh, Gelu M. Nita, Patrick M. O'Keefe, Chun Jie Chong, Paul Kosovich, Russell D. Marroquin, Berkay Aydin, Petrus C. Martens, Lulu Zhao, Yang Chen, Yian Yu, Monica G. Bobra, Ward Manchester, Tamas Gombosi, Ming Zhang, Jesse Torres, Philip K. Chan, Mohamed Nedal, Kamen Kozarev, Peijin Zhang, Kimberly Moreland, Hazel M. Bain, Samuel Hart, Michael J. Starkey, Alan G. Ling, Simone Benella
机构
*
Department of Astrophysical Sciences, Princeton University, Princeton, NJ, USA
；
Computational Physics Branch, NASA Ames Research Center, Moffett Field, CA, USA
；
Department of Computer Science, Utah State University, Logan, UT, USA
；
Space Radiation Analysis Group, NASA Johnson Space Center, Houston, TX, USA
；
Johns Hopkins Applied Physics Lab, 11100 Johns Hopkins Rd, Laurel, MD 20723, United States
；
Research Center for Astronomy
；
Applied Mathematics of the Academy of Athens, 4 Soranou Efesiou Street, Athens 11527, Greece
；
Institute for Astronomy, Astrophysics, Space Applications
；
Southwest Research Institute, Boulder, CO, USA
；
Space Science Center, University of New Hampshire, Durham, NH, USA
；
Department of Physics, New Jersey Institute of Technology, Newark, NJ, USA
；
Astronomy Department, Georgia State University, Atlanta, GA, USA
；
Department of Computer Science, Princeton University, Princeton, NJ, USA
；
Department of Mathematics, Rowan University, Glassboro, NJ, USA
；
Astronomy, California Institute of Technology, Pasadena, CA, USA
；
Department of Physics, National
；
Kapodistrian University of Athens, Athens, Greece
；
School of Electrical
；
Computer Engineering, Technical University of Crete, Chania, Greece
；
Department of Astronomy
；
Meteorology, Faculty of Science, Al-Azhar University, Cairo, Egypt
；
Space Sciences Lab, University of California, Berkeley, CA, USA
；
Research Consultancy, Athens, Greece
；
Institute for Space Astrophysics
；
Department of Physics
；
Astronomy, Georgia State University, Atlanta, GA 30303, USA
；
Aryabhatta Research Institute of Observational Sciences (ARIES), Manora Peak, Nainital-263001, Uttarakhand, India
；
Department of Computer Science, Oxford University, Oxford, England
；
Southwest Research Institute, San Antonio, TX, USA
；
Computer Science Department, New Jersey Institute of Technology, Newark, NJ, USA
；
Department of Physics, University of California San Diego, La Jolla, CA 92093, USA
；
Department of Computer Science, Georgia State University, Atlanta, GA 30303, USA
；
Department of Climate
；
Engineering, University of Michigan, Ann Arbor, MI, USA
；
Department of Statistics, University of Michigan, Ann Arbor, MI, USA
；
Department of Electrical Engineering
；
Computer Science, Florida Institute of Technology, Melbourne, FL, USA
；
Astrophysics Section, School of Cosmic Physics, Dublin Institute for Advanced Studies, DIAS Dunsink Observatory, Dublin D15 XR2R, Ireland
；
Institute of Astronomy of the Bulgarian Academy of Sciences, Sofia, Bulgaria
；
Center for Solar-Terrestrial Research, New Jersey Institute of Technology, Newark, NJ 07102, USA
；
Cooperative Programs for the Advancement of Earth System Science, University Corporation for Atmospheric Research, Boulder, CO, USA
；
CIRES, University of Colorado Boulder, Boulder, CO, USA
；
Space Weather Prediction Center, NOAA, Boulder, CO, USA
；
Astronomy, College of Science, The University of Texas at San Antonio, San Antonio, TX, USA
；
Space Weather Prediction Center, National Oceanic
；
The University of Texas at San Antonio, San Antonio, TX, USA
；
Environmental Research, Inc., MA, USA
机构
*
Department of Computing and Mathematical Sciences, California Institute of Technology（计算与数学科学系，加州理工学院）
；
Department of Statistical Sciences, University of Toronto（统计科学系，多伦多大学）
机构
*
College of Computer Science and Technology, Zhejiang University, Hangzhou, China（浙江大学计算机科学与技术学院，中国杭州）
；
WINDY Lab, Department of Artificial Intelligence, Westlake University, Hangzhou, China（西湖大学人工智能系WINDY实验室，中国杭州）
；
Department of Electrical Engineering, California Institute of Technology, Pasadena, USA（加州理工学院电气工程系，美国帕萨迪纳）
Pruning Deep Neural Networks via the Marchenko--Pastur Distribution
通过Marchenko-Pastur分布剪枝深度神经网络
Leonid Berlyand, Theo Bourdais, Houman Owhadi, Yitzchak Shmalo
机构
*
Department of Mathematics, Pennsylvania State University（数学系，宾夕法尼亚州立大学）
；
Department of Computing and Mathematical Sciences, California Institute of Technology（计算与数学科学系，加州理工学院）
Karan Gandhi, Ashish A. Mahabal, Jacob E. Jencson, Russ R. Laher, Ben Rusholme, Lin Yan, Ryan M. Lau, Schuyler D. Van Dyk, Mansi M. Kasliwal
机构
*
Department of Computer Science and Engineering, Indian Institute of Technology, Gandhinagar, India（印度理工学院计算机科学与工程系）
；
Division of Physics, Mathematics, and Astronomy, California Institute of Technology, Pasadena, CA 91125, USA（加州理工学院物理、数学与天文学系）
；
Center for Data Driven Discovery, California Institute of Technology, Pasadena, CA 91125, USA（数据驱动发现中心）
；
IPAC, California Institute of Technology, 1200 E. California Blvd, Pasadena, CA 91125, USA（IPAC, 加州理工学院）
；
Caltech Optical Observatories, California Institute of Technology, Pasadena, CA 91125, USA（加州理工学院光学观测站）
Derivative Informed Learning of Exchange-Correlation Functionals
交换相关泛函的导数知情学习
Eike S. Eberhard, Luca A. Thiede, Abdul Aldossary, Andreas Burger, Nicholas Gao, Vignesh Bhethanabotla, Alán Aspuru-Guzik, Stephan Günnemann
机构
*
Technical University of Munich（慕尼黑技术大学）
；
Munich Data Science Institute（慕尼黑数据科学研究所）
；
Munich Center for Machine Learning（慕尼黑机器学习中心）
；
University of Toronto（多伦多大学）
；
Vector Institute（向量研究所）
；
CuspAI
；
California Institute of Technology（加州理工学院）
