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Titlebook: Computational Science – ICCS 2023; 23rd International C Jiří Mikyška,Clélia de Mulatier,Peter M.A. Sloot Conference proceedings 2023 The Ed

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发表于 2025-3-21 16:37:22 | 显示全部楼层 |阅读模式
书目名称Computational Science – ICCS 2023
副标题23rd International C
编辑Jiří Mikyška,Clélia de Mulatier,Peter M.A. Sloot
视频video
丛书名称Lecture Notes in Computer Science
图书封面Titlebook: Computational Science – ICCS 2023; 23rd International C Jiří Mikyška,Clélia de Mulatier,Peter M.A. Sloot Conference proceedings 2023 The Ed
描述.The five-volume set LNCS 14073-14077 constitutes the proceedings of the 23rd International Conference on Computational Science, ICCS 2023, held in Prague, Czech Republic, during July 3-5, 2023...The total of 188 full papers and 94 short papers presented in this book set were carefully reviewed and selected from 530 submissions. 54 full and 37 short papers were accepted to the main track; 134 full and 57 short papers were accepted to the workshops/thematic tracks...The theme for 2023, "Computation at the Cutting Edge of Science.",. highlights the role of Computational Science in assisting multidisciplinary research. This conference was a unique event focusing on recent developments in scalable scientific algorithms, advanced software tools; computational grids; advanced numerical methods; and novel application areas. These innovative novel models, algorithms, and tools drive new science through efficient application in physical systems, computational andsystems biology, environmental systems, finance, and others..
出版日期Conference proceedings 2023
关键词Modelling; Simulation; Big Data; Computational Science; Computational Methods; Numerical Algorithms; Compl
版次1
doihttps://doi.org/10.1007/978-3-031-35995-8
isbn_softcover978-3-031-35994-1
isbn_ebook978-3-031-35995-8Series ISSN 0302-9743 Series E-ISSN 1611-3349
issn_series 0302-9743
copyrightThe Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Switzerl
The information of publication is updating

书目名称Computational Science – ICCS 2023影响因子(影响力)




书目名称Computational Science – ICCS 2023影响因子(影响力)学科排名




书目名称Computational Science – ICCS 2023网络公开度




书目名称Computational Science – ICCS 2023网络公开度学科排名




书目名称Computational Science – ICCS 2023被引频次




书目名称Computational Science – ICCS 2023被引频次学科排名




书目名称Computational Science – ICCS 2023年度引用




书目名称Computational Science – ICCS 2023年度引用学科排名




书目名称Computational Science – ICCS 2023读者反馈




书目名称Computational Science – ICCS 2023读者反馈学科排名




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https://doi.org/10.1007/978-981-19-0371-7omplete complex maneuvers were performed using a meta-controller capable of sequencing the basic ones learned by imitation. This endeavor was rewarded by the results that show several Machine Learning models capable of performing highly complex aircraft maneuvers.
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Downscaling WRF-Chem: Analyzing Urban Air Quality in Barcelona Citygy. Using the city of Barcelona as a case study, this paper confirms that the modeling methodology used up to now must be reviewed to design green cities. Certain limitations of the WRF-Chem model have been analyzed, including the BEP-BEM as an urban canopy layer, and the reasons for such limitations are discussed.
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Performing Aerobatic Maneuver with Imitation Learningomplete complex maneuvers were performed using a meta-controller capable of sequencing the basic ones learned by imitation. This endeavor was rewarded by the results that show several Machine Learning models capable of performing highly complex aircraft maneuvers.
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Takemi Sugiyama,Maike Neuhaus,Neville Owentions without actually solving a system of linear equations as it happens in traditional numerical methods like, e.g., finite element or finite difference method. In particular, we compare the influence of selected activation functions on the convergence of the PINN method. Our PINN code is available on github: ..
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Sustainable Environmental Geotechnicsour method allows for the identification of the automaton of size . when it exists. While the concept may seem straightforward, the effectiveness of this approach is demonstrated through the results of our experiments.
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Sneha Bandyopadhyay,Subodh Kumar Maiti contrastive learning to gather similar users and enhance user interest encoding. Then, we can match items and user popularity preferences more accurately and make more proper recommendations. Extensive experiments validate that UPSR not only outperforms the state-of-the-art methods but also reduces popularity bias.
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