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检索条件"机构=System Modeling and Optimization Lab"
4 条 记 录,以下是1-10 订阅
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Reliability Polynomial for Rectangular Lattice Strip
Reliability Polynomial for Rectangular Lattice Strip
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2022 IEEE International Multi-Conference on Engineering, Computer and Information Sciences, SIBIRCON 2022
作者: Shakhov, Vladimir Chen, Honglong Rodionov, Alexey Novosibirsk State Technical University ICM&MG SB RAS Novosibirsk Russia China University of Petroleum Control Science and Engineering College Qingdao China ICM&MG SB RAS System Modeling and Optimization Lab Novosibirsk Russia
The study of graphs reliability polynomials is of both theoretical and practical interest. Generally, the problem of reliability polynomial calculation is NP-hard. In this paper we find analytic solution of this probl... 详细信息
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Reliability Polynomial for Rectangular Lattice Strip
Reliability Polynomial for Rectangular Lattice Strip
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IEEE Region 8 International Conference on Computational Technologies in Electrical and Electronics Engineering, SIBIRCON
作者: Vladimir Shakhov Honglong Chen Alexey Rodionov ICM&MG SB RAS Novosibirsk State Technical University Novosibirsk Russia Control Science and Engineering College China University of Petroleum Qingdao China System Modeling and Optimization Lab ICM&MG SB RAS Novosibirsk Russia
The study of graphs reliability polynomials is of both theoretical and practical interest. Generally, the problem of reliability polynomial calculation is NP-hard. In this paper we find analytic solution of this probl... 详细信息
来源: 评论
Some New Ideas About Obtaining and Estimating Reliability Polynomial of a Random Graph
Some New Ideas About Obtaining and Estimating Reliability Po...
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International Conference on Ubiquitous Information Management and Communication (IMCOM)
作者: Alexey S. Rodionov System modeling and optimization Lab. Institute of Computational Mathematics and Mathematical Geophysics SB RAS Novosibirsk Russia
In this paper some new ideas are presented concerning speeding up calculation of coefficients of all-terminal reliability polynomial (CRPs) of an undirected random graph with reliable nodes and unreliable links. Along... 详细信息
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Modern applications of machine learning in quantum sciences
arXiv
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arXiv 2022年
作者: Dawid, Anna Arnold, Julian Requena, Borja Gresch, Alexander Plodzien, Marcin Donatella, Kaelan Nicoli, Kim A. Stornati, Paolo Koch, Rouven Büttner, Miriam Okula, Robert Muñoz–Gil, Gorka Vargas–Hernández, Rodrigo A. Cervera-Lierta, Alba Carrasquilla, Juan Dunjko, Vedran Gabrié, Marylou Huembeli, Patrick van Nieuwenburg, Evert Vicentini, Filippo Wang, Lei Wetzel, Sebastian J. Carleo, Giuseppe Greplová, Eliška Krems, Roman Marquardt, Florian Tomza, Michal Lewenstein, Maciej Dauphin, Alexandre Faculty of Physics University of Warsaw Poland ICFO - Institut de Ciències Fotòniques The Barcelona Institute of Science and Technology Castelldefels Barcelona08860 Spain Center for Computational Quantum Physics Flatiron Institute New York United States Department of Physics University of Basel Switzerland Institute for Theoretical Physics Heinrich Heine University Düsseldorf Germany Institute for Quantum Inspired and Quantum Optimization Hamburg University of Technology Germany Université de Paris CNRS Laboratoire Matériaux et Phénomènes Quantiques France Machine Learning Group Technische Universität Berlin Germany BIFOLD Berlin Institute for the Foundations of Learning and Data Berlin10587 Germany Department of Applied Physics Aalto University Espoo Finland Institute of Physics Albert-Ludwig University of Freiburg Germany International Centre for Theory of Quantum Technologies University of Gdańsk Poland Department of Algorithms and System Modeling Faculty of Electronics Faculty of Electronics Telecommunications and Informatics Gdańsk University of Technology Poland Institute for Theoretical Physics University of Innsbruck Austria Department of Chemistry University of Toronto Canada Vector Institute for Artificial Intelligence MaRS Centre Toronto Canada Department of Chemistry and Chemical Biology McMaster University Hamilton Canada Barcelona Supercomputing Center Spain LIACS Leiden University Netherlands CMAP École Polytechnique France Switzerland Menten AI Inc. Palo AltoCA United States Niels Bohr Institute Copenhagen Denmark CPHT CNRS École Polytechnique Institut Polytechnique de Paris PalaiseauF-91128 France Beijing National Lab for Condensed Matter Physics Institute of Physics Chinese Academy of Sciences Beijing China Songshan Lake Materials Laboratory Dongguan China Perimeter Institute for Theoretical Physics Waterloo Canada Kavli Institute of Nanoscience Delft University of Technology DelftNL-2600 GA Netherlands Department of
In this book, we provide a comprehensive introduction to the most recentadvances in the application of machine learning methods in quantum sciences. Wecover the use of deep learning and kernel methods in supervised, u... 详细信息
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