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Titlebook: Computational Science – ICCS 2024; 24th International C Leonardo Franco,Clélia de Mulatier,Peter M. A. Slo Conference proceedings 2024 The

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发表于 2025-3-21 17:27:12 | 显示全部楼层 |阅读模式
书目名称Computational Science – ICCS 2024
副标题24th International C
编辑Leonardo Franco,Clélia de Mulatier,Peter M. A. Slo
视频videohttp://file.papertrans.cn/243/242276/242276.mp4
丛书名称Lecture Notes in Computer Science
图书封面Titlebook: Computational Science – ICCS 2024; 24th International C Leonardo Franco,Clélia de Mulatier,Peter M. A. Slo Conference proceedings 2024 The
描述.The 7-volume set LNCS 14832 – 14838 constitutes the proceedings of the 24th International Conference on Computational Science, ICCS 2024, which took place in Malaga, Spain, during July 2–4, 2024...The 155 full papers and 70 short papers included in these proceedings were carefully reviewed and selected from 430 submissions...They were organized in topical sections as follows:..Part I:. ICCS 2024 Main Track Full Papers;..Part II:. ICCS 2024 Main Track Full Papers;..Part III:. ICCS 2024 Main Track Short Papers; Advances in High-Performance Computational Earth Sciences: Numerical Methods, Frameworks and Applications; Artificial Intelligence and High-Performance Computing for Advanced Simulations;..Part IV:. Biomedical and Bioinformatics Challenges for Computer Science; Computational Health;..Part V.: Computational Optimization, Modelling, and Simulation; Generative AI and Large Language Models (LLMs) in Advancing Computational Medicine; Machine Learning and Data Assimilation for Dynamical Systems; Multiscale Modelling and Simulation;..Part VI:. Network Models and Analysis: From Foundations to Artificial Intelligence; Numerical Algorithms and Computer Arithmetic for Computational Scie
出版日期Conference proceedings 2024
关键词Modelling; Simulation; Big Data; Computational Science; Computational Methods; Numerical Algorithms; Compl
版次1
doihttps://doi.org/10.1007/978-3-031-63783-4
isbn_softcover978-3-031-63785-8
isbn_ebook978-3-031-63783-4Series 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 2024影响因子(影响力)




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书目名称Computational Science – ICCS 2024被引频次学科排名




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书目名称Computational Science – ICCS 2024读者反馈学科排名




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Mark Adler,Pierre van Moerbeke,Pol Vanhaeckeation procedure. Finally, our simulation results indicate that the miscibility of fluids significantly influences capillary behaviors, highlighting the necessity of species-specific compositional modeling for miscible two-phase fluids in porous media.
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Algebraic K-Theory and Its Applications. Based on . experimental data, the deformation and associated stress of a low shear aggregate and a high shear aggregate under different flow conditions are evaluated. This FSI framework offers a way to elucidate the complex interaction between blood flow and platelet aggregates and is applicable t
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https://doi.org/10.1007/978-1-4612-4314-4 a step height of H = L/3 (where L is the channel height) at Reynolds number Re = 132000 was investigated using finite-element solutions of the GHE. The results were compared to Direct Numerical Simulations (DNS) utilizing the Navier-Stokes equations, and to a . turbulence model, as well as experime
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Definition of G(R) and examples,ing mask. Additionally, the distance droplets disperse increases over time when mask deformation is considered. As a practical application, we analyzed droplet dispersion in a scenario where a wheelchair is being pushed, utilizing airflow data that accounts for mask deformation. This analysis indica
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Hajnal Andréka,István Németi,Ildikó Saintation for the urban sprawl evaluation using targeted land cover map construction. The proposed method monitors the expansion of the city, which, uncontrolled, can cause adverse effects. The method was tested on photos from three residential districts. The first district has been manually segmented
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https://doi.org/10.1007/978-1-84628-595-0d edges represent relationships. We then propose a relational graph aggregation method to learn node representations by sampling and aggregating the features from both direct and indirect neighbors. Additionally, we evaluate the importance of different relations and fuse the node’s representations a
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