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Titlebook: Computational Science – ICCS 2020; 20th International C Valeria V. Krzhizhanovskaya,Gábor Závodszky,Jo?o T Conference proceedings 2020 Spri

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發(fā)表于 2025-3-21 17:57:03 | 只看該作者 |倒序瀏覽 |閱讀模式
書目名稱Computational Science – ICCS 2020
副標題20th International C
編輯Valeria V. Krzhizhanovskaya,Gábor Závodszky,Jo?o T
視頻videohttp://file.papertrans.cn/234/233089/233089.mp4
叢書名稱Lecture Notes in Computer Science
圖書封面Titlebook: Computational Science – ICCS 2020; 20th International C Valeria V. Krzhizhanovskaya,Gábor Závodszky,Jo?o T Conference proceedings 2020 Spri
描述.The seven-volume set LNCS 12137, 12138, 12139, 12140, 12141, 12142, and 12143 constitutes the proceedings of the 20th International Conference on Computational Science, ICCS 2020, held in Amsterdam, The Netherlands, in June 2020.*..The total of 101 papers and 248 workshop papers presented in this book set were carefully reviewed and selected from 719 submissions (230 submissions to the main track and 489 submissions to the workshops). The papers were organized in topical sections named:.Part I: ICCS Main Track..Part II: ICCS Main Track..Part III: Track of Advances in High-Performance Computational Earth Sciences: Applications and Frameworks; Track of Agent-Based Simulations, Adaptive Algorithms and Solvers; Track of Applications of Computational Methods in Artificial Intelligence and Machine Learning; Track of Biomedical and Bioinformatics Challenges for Computer Science..Part IV: Track of Classifier Learning from Difficult Data; Track of Complex Social Systems through the Lens of Computational Science; Track of Computational Health; Track of Computational Methods for Emerging Problems in (Dis-)Information Analysis..Part V: Track of Computational Optimization, Modelling and Simula
出版日期Conference proceedings 2020
關(guān)鍵詞artificial intelligence; communication systems; computer hardware; computer networks; correlation analys
版次1
doihttps://doi.org/10.1007/978-3-030-50426-7
isbn_softcover978-3-030-50425-0
isbn_ebook978-3-030-50426-7Series ISSN 0302-9743 Series E-ISSN 1611-3349
issn_series 0302-9743
copyrightSpringer Nature Switzerland AG 2020
The information of publication is updating

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Conference proceedings 2020 of Classifier Learning from Difficult Data; Track of Complex Social Systems through the Lens of Computational Science; Track of Computational Health; Track of Computational Methods for Emerging Problems in (Dis-)Information Analysis..Part V: Track of Computational Optimization, Modelling and Simula
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Reduced-Cost Constrained Modeling of Microwave and Antenna Components: Recent Advancesgion contains the parameter vectors corresponding to high-quality designs w.r.t. the considered figures of interest, which allows for a dramatic reduction of the number of training samples needed to render reliable surrogates without formally restricting the parameter ranges. This paper reviews the
地板
發(fā)表于 2025-3-22 07:40:43 | 只看該作者
Aerodynamic Shape Optimization for?Delaying Dynamic Stall of Airfoils by?Regression Kriging samples are used to construct the metamodel, which is further refined with 20 infill points using expected improvement. The metamodel is validated with the normalized root mean square error based on 20 test data samples. The refined metamodel is used to search for the optimal design using a multi-s
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Fully-Asynchronous Fully-Implicit Variable-Order Variable-Timestep Simulation of Neural Networkss’ time advancement to synaptic connectivities. Timestepping is driven by scheduled neuron advancements based on interneuron synaptic delays, yielding an . execution. Benchmarks on 64 Cray XE6 compute nodes demonstrate reduced number of interpolation steps, higher numerical accuracy and lower runtim
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Genetic Learning Particle Swarm Optimization with Interlaced Ring Topologytopology has been integrated with GL-PSO only (referred to as GL-PSOI) as well as with a flexible local search operator (referred to as GL-PSOIF). The new strategy was tested on a set of benchmark problems and a CEC2014 test suite. The results were compared with five different variants of PSO, inclu
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A Stochastic Birth-Death Model of Information Propagation Within Human Networks to multiple information recipients or listeners, each independent of one another, per a binomial distribution. We find that while in mixed populations, the fixation probability of the mutant message is strongly correlated with the talkativity (sample correlation .) and belief probability (.) of the
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