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检索条件"机构=Program in Computer Science and Division of Applied Mathematics"
1714 条 记 录,以下是291-300 订阅
Derivative-free optimization of a rapid-cycling synchrotron
arXiv
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arXiv 2021年
作者: Eldred, Jeffrey S. Larson, Jeffrey Padidar, Misha Stern, Eric Wild, Stefan M. Fermi National Accelerator Laboratory BataviaIL United States Mathematics and Computer Science Division Argonne National Laboratory LemontIL United States Center for Applied Mathematics Cornell University IthacaNY United States
We develop and solve a constrained optimization model to identify an integrable optics rapid-cycling synchrotron lattice design that performs well in several capacities. Our model encodes the design criteria into 78 l... 详细信息
来源: 评论
Incorporating Prior Knowledge into Neural Networks through an Implicit Composite Kernel
arXiv
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arXiv 2022年
作者: Jiang, Ziyang Zheng, Tongshu Liu, Yiling Carlson, David Department of Civil and Environmental Engineering Duke University United States Division of Natural and Applied Science Duke Kunshan University China Program in Computational Biology and Bioinformatics Duke University School of Medicine United States Department of Civil and Environmental Engineering Department of Biostatistics and Bioinformatics Department of Computer Science Duke University United States
It is challenging to guide neural network (NN) learning with prior knowledge. In contrast, many known properties, such as spatial smoothness or seasonality, are straightforward to model by choosing an appropriate kern... 详细信息
来源: 评论
Enabling equation-free modeling via diffusion maps
arXiv
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arXiv 2021年
作者: Chin, Tracy Ruth, Jacob Sanford, Clayton Santorella, Rebecca Carter, Paul Sandstede, Björn Department of Mathematics University of Washington Seattle United States Department of Computer Science Columbia University New York City United States Division of Applied Mathematics Brown University Providence United States Department of Mathematics University of California Irvine United States
Equation-free modeling aims at extracting low-dimensional macroscopic dynamics from complex high-dimensional systems that govern the evolution of microscopic states. This algorithm relies on lifting and restriction op...
来源: 评论
PHYSICS INFORMED MACHINE LEARNING WITH SMOOTHED PARTICLE HYDRODYNAMICS: HIERARCHY OF REDUCED LAGRANGIAN MODELS OF TURBULENCE
arXiv
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arXiv 2021年
作者: Woodward, Michael Tian, Yifeng Hyett, Criston Fryer, Chris Stepanov, Mikhail Livescu, Daniel Chertkov, Michael Graduate Interdisciplinary Program in Applied Mathematics UArizona TucsonAZ85721 United States Department of Mathematics UArizona TucsonAZ85721 United States Computer Computational and Statistical Sciences Division LANL Los AlamosNM87544 United States
Building efficient, accurate and generalizable reduced order models of developed turbulence remains a major challenge. This manuscript approaches this problem by developing a hierarchy of parameterized reduced Lagrang... 详细信息
来源: 评论
Correction: Feynman checkers: lattice quantum field theory with real time
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Analysis and Mathematical Physics 2024年 第3期14卷 1-1页
作者: Skopenkov, M. Ustinov, A. King Abdullah University of Science and Technology Thuwal Saudi Arabia Faculty of Computer Science HSE University Khabarovsk Division of the Institute for Applied Mathematics Far-Eastern Branch Russian Academy of Sciences Moscow Russia
来源: 评论
MatterChat: A Multi-Modal LLM for Material science
arXiv
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arXiv 2025年
作者: Tang, Yingheng Xu, Wenbin Cao, Jie Gao, Weilu Farrell, Steven Erichson, Benjamin Mahoney, Michael W. Nonaka, Andy Yao, Zhi Applied Mathematics and Computational Research Division Lawrence Berkeley National Laboratory BerkeleyCA United States National Energy Research Scientific Computing Center Lawrence Berkeley National Laboratory BerkeleyCA United States NSF National AI Institute for Student-AI Teaming University of Colorado at Boulder Boulder United States Department of Electrical and Computer Engineering The University of Utah Salt Lake CityUT United States Scientific Data Division Lawrence Berkeley National Laboratory BerkeleyCA United States International Computer Science Institute BerkeleyCA United States Department of Statistics University of California at Berkeley BerkeleyCA United States
Understanding and predicting the properties of inorganic materials is crucial for accelerating advancements in materials science and driving applications in energy, electronics, and beyond. Integrating material struct... 详细信息
来源: 评论
EXPLICIT RUNGE–KUTTA METHODS THAT ALLEVIATE ORDER REDUCTION
arXiv
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arXiv 2023年
作者: Biswas, Abhijit Ketcheson, David I. Roberts, Steven Seibold, Benjamin Shirokoff, David Computer Electrical and Mathematical Sciences & Engineering Division King Abdullah University of Science and Technology Thuwal23955 Saudi Arabia Center for Applied Scientific Computing Lawrence Livermore National Laboratory LivermoreCA94550 United States Department of Mathematics Temple University PhiladelphiaPA19122 United States Department of Mathematical Sciences New Jersey Institute of Technology NewarkNJ07102 United States
Explicit Runge–Kutta (RK) methods are susceptible to a reduction in the observed order of convergence when applied to initial-boundary value problem with time-dependent boundary conditions. We study conditions on exp... 详细信息
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Importance of Kernel Bandwidth in Quantum Machine Learning
arXiv
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arXiv 2021年
作者: Shaydulin, Ruslan Wild, Stefan M. Global Technology Applied Research JPMorgan Chase New YorkNY10017 United States Mathematics and Computer Science Division Argonne National Laboratory LemontIL60439 United States
Quantum kernel methods are considered a promising avenue for applying quantum computers to machine learning problems. Identifying hyperparameters controlling the inductive bias of quantum machine learning models is ex... 详细信息
来源: 评论
Utilizing probabilistic entanglement between sensors in quantum networks
arXiv
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arXiv 2024年
作者: Van Milligen, Emily A. Gagatsos, Christos N. Kaur, Eneet Towsley, Don Guha, Saikat Department of Physics The University of Arizona 1630 East University Boulevard TucsonAZ85721 United States Department of Electrical and Computer Engineering The University of Arizona 1630 East University Boulevard TucsonAZ85721 United States Wyant College of Optical Sciences The University of Arizona 1630 East University Boulevard TucsonAZ85721 United States Program in Applied Mathematics The University of Arizona TucsonAZ85721 United States Cisco Quantum Lab Los Angeles United States College of Computer Science University of Massachusetts AmherstMA United States
One of the most promising applications of quantum networks is entanglement assisted sensing. The field of quantum metrology exploits quantum correlations to improve the precision bound for applications such as precisi... 详细信息
来源: 评论
Adaptive tikhonov strategies for stochastic ensemble Kalman inversion
arXiv
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arXiv 2021年
作者: Weissmann, Simon Chada, Neil K. Schillings, Claudia Tong, Xin T. Interdisciplinary Center for Scientific Computing University of Heidelberg Heidelberg69120 Germany Applied Mathematics and Computational Science Program King Abdullah University of Science and Technology Thuwal KSA 23955 Saudi Arabia Mannheim School of Computer Science and Mathematics University of Mannheim Mannheim68131 Germany Department of Mathematics National University of Singapore Singapore119077 Singapore
Ensemble Kalman inversion (EKI) is a derivative-free optimizer aimed at solving inverse problems, taking motivation from the celebrated ensemble Kalman filter. The purpose of this article is to consider the introducti... 详细信息
来源: 评论