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检索条件"机构=Physics Department and Program in Applied and Computational Mathematics"
3229 条 记 录,以下是651-660 订阅
排序:
Laboratory investigation of the jet deflection: collisionless to strong-collision
arXiv
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arXiv 2022年
作者: Lei, Zhu Zhao, Z.H. Li, X.Y. Li, L.X. Xie, Y. Yuan, W.Q. Zhu, B.Q. Zhu, J.Q. Zhu, S.P. He, X.T. Qiao, B. Center for Applied Physics and Technology HEDPS State Key Laboratory of Nuclear Physics and Technology School of Physics Peking University Beijing100871 China Institute of Applied Physics and Computational Mathematics Beijing100094 China Department of Plasma Physics and Fusion Engineering CAS Key Laboratory of Geospace Environment University of Science and Technology of China Anhui Hefei230026 China National Laboratory on High Power Laser and Physics Chinese Academy of Sciences Shanghai201800 China
The interaction between a jet and its surrounding medium plays a crucial role in various astrophysical phenomena, notably in star formation and jet deflection. Despite its significance, the detailed processes and fine... 详细信息
来源: 评论
Algebraic Compression of Free Fermionic Quantum Circuits: Particle Creation, Arbitrary Lattices and Controlled Evolution
arXiv
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arXiv 2023年
作者: Kökcü, Efekan Camps, Daan Oftelie, Lindsay Bassman de Jong, Wibe A. Van Beeumen, Roel Kemper, A.F. Department of Physics North Carolina State University RaleighNC27695 United States National Energy Research Scientific Computing Center Lawrence Berkeley National Laboratory BerkeleyCA94720 United States Applied Mathematics and Computational Research Division Lawrence Berkeley National Laboratory BerkeleyCA94720 United States
Recently we developed a local and constructive algorithm based on Lie algebraic methods for compressing Trotterized evolution under Hamiltonians that can be mapped to free fermions [1, 2]. The compression algorithm yi... 详细信息
来源: 评论
KOLMOGOROV WIDTH DECAY AND POOR APPROXIMATORS IN MACHINE LEARNING: SHALLOW NEURAL NETWORKS, RANDOM FEATURE MODELS AND NEURAL TANGENT KERNELS
arXiv
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arXiv 2020年
作者: Weinan, E. Wojtowytsch, Stephan Department of Mathematics and Program in Applied and Computational Mathematics Princeton University PrincetonNJ08544 United States Program in Applied and Computational Mathematics Princeton University PrincetonNJ08544
We establish a scale separation of Kolmogorov width type between subspaces of a given Banach space under the condition that a sequence of linear maps converges much faster on one of the subspaces. The general techniqu... 详细信息
来源: 评论
Coarse-grained spectral projection (CGSP): A deep learning-assisted approach to quantum unitary dynamics
arXiv
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arXiv 2020年
作者: Xie, Pinchen Weinan, E. Program in Applied and Computational Mathematics Princeton University NJ08544 United States Department of Mathematics and Program in Applied and Computational Mathematics Princeton University NJ08544 United States
We propose the coarse-grained spectral projection method (CGSP), a deep learning-assisted approach for tackling quantum unitary dynamic problems with an emphasis on quench dynamics. We show CGSP can extract spectral c... 详细信息
来源: 评论
DeePKS-kit: A package for developing machine learning-based chemically accurate energy and density functional models
arXiv
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arXiv 2020年
作者: Chen, Yixiao Zhang, Linfeng Wang, Han Weinan, E. Program in Applied and Computational Mathematics Princeton University PrincetonNJ United States Laboratory of Computational Physics Institute of Applied Physics and Computational Mathematics Huayuan Road 6 Beijing100088 China Department of Mathematics Princeton University PrincetonNJ United States
We introduce DeePKS-kit, an open-source software package for developing machine learning based energy and density functional models. DeePKS-kit is interfaced with PyTorch, an open-source machine learning library, and ... 详细信息
来源: 评论
DeePKS: A comprehensive data-driven approach towards chemically accurate density functional theory
arXiv
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arXiv 2020年
作者: Chen, Yixiao Zhang, Linfeng Wang, Han Weinan, E. Program in Applied and Computational Mathematics Princeton University PrincetonNJ United States Laboratory of Computational Physics Institute of Applied Physics and Computational Mathematics Huayuan Road 6 Beijing100088 China Department of Mathematics Princeton University PrincetonNJ United States
We propose a general machine learning-based framework for building an accurate and widely-applicable energy functional within the framework of generalized Kohn-Sham density functional theory. To this end, we develop a... 详细信息
来源: 评论
Regimes of stratified turbulence at low Prandtl number
arXiv
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arXiv 2023年
作者: Shah, Kasturi Chini, Gregory P. Caulfield, Colm-Cille P. Garaud, Pascale Department of Applied Mathematics and Theoretical Physics University of Cambridge CambridgeCB3 0WA United Kingdom Department of Earth Atmospheric and Planetary Sciences Massachusetts Institute of Technology CambridgeMA02139 United States Program in Integrated Applied Mathematics University of New Hampshire DurhamNH03824 United States Department of Mechanical Engineering University of New Hampshire DurhamNH03824 United States Institute for Energy and Environmental Flows University of Cambridge CambridgeCB3 0EZ United Kingdom Department of Applied Mathematics Baskin School of Engineering University of California Santa Cruz Santa CruzCA95064 United States
Quantifying transport by strongly stratified turbulence in low Prandtl number (Pr) fluids is critically important for the development of better models for the structure and evolution of stellar and planetary interiors... 详细信息
来源: 评论
Fast Universal Entangling Gate for Superconducting Quantum Computers
SSRN
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SSRN 2024年
作者: AbuGhanem, Muhammad Homid, Ali H. Obada, A.S.-F. Hendy, Ahmed S. Eleuch, Hichem Faculty of Science Ain Shams University Cairo11566 Egypt Zewail City of Science Technology and Innovation Giza12678 Egypt Mathematics Department Faculty of Science Al-Azhar University Assiut71524 Egypt Mathematics Department Faculty of Science Al-Azher University Nassr City Cairo11884 Egypt Department of Computational Mathematics and Computer Science Institute of Natural Sciences and Mathematics Ural Federal University Yekaterinburg Russia Department of Mathematics Faculty of Science Benha University Benha Egypt Department of Applied Physics and Astronomy University of Sharjah Sharjah27272 United Arab Emirates College of Arts and Sciences Abu Dhabi University Abu Dhabi59911 United Arab Emirates Institute for Quantum Science and Engineering Texas A&M University College StationTX77843 United States
Universal quantum gates are crucial for efficient algorithm execution on quantum computers, offering rapid and fault-tolerant manipulations. This paper presents a theoretical framework for realizing a novel entangling... 详细信息
来源: 评论
Ground state energy functional with Hartree-Fock efficiency and chemical accuracy
arXiv
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arXiv 2020年
作者: Chen, Yixiao Zhang, Linfeng Wang, Han Weinan, E. Program in Applied and Computational Mathematics Princeton University PrincetonNJ United States Laboratory of Computational Physics Institute of Applied Physics and Computational Mathematics Huayuan Road 6 Beijing100088 China Department of Mathematics Princeton University PrincetonNJ United States
We introduce the Deep Post–Hartree–Fock (DeePHF) method, a machine learning-based scheme for constructing accurate and transferable models for the ground-state energy of electronic structure problems. DeePHF predict... 详细信息
来源: 评论
Bayesian calibration of viscous anisotropic hydrodynamic simulations of heavy-ion collisions
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Physical Review C 2023年 第5期108卷 054905-054905页
作者: Dananjaya Liyanage Özge Sürer Matthew Plumlee Stefan M. Wild Ulrich Heinz Department of Physics The Ohio State University Columbus Ohio 43210 USA Department of Information Systems and Analytics Miami University Oxford Ohio 45056 USA Northwestern-Argonne Institute of Science and Engineering (NAISE) Northwestern University Evanston Illinois 60208 USA Industrial Engineering and Management Sciences Department Northwestern University Evanston Illinois 60208 USA Applied Mathematics & Computational Research Division Lawrence Berkeley National Laboratory Berkeley California 94720 USA
Owing to large pressure gradients at early times, standard hydrodynamic model simulations of relativistic heavy-ion collisions do not become reliable until O(1) fm/c after the collision. To address this one often intr... 详细信息
来源: 评论