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Titlebook: Accelerating Science and Engineering Discoveries Through Integrated Research Infrastructure for Expe; 22nd Smoky Mountains Kothe Doug,Geist

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發(fā)表于 2025-3-21 16:18:06 | 只看該作者 |倒序瀏覽 |閱讀模式
期刊全稱Accelerating Science and Engineering Discoveries Through Integrated Research Infrastructure for Expe
期刊簡稱22nd Smoky Mountains
影響因子2023Kothe Doug,Geist Al,Suzanne Parete-Koon
視頻videohttp://file.papertrans.cn/144/143648/143648.mp4
學(xué)科分類Communications in Computer and Information Science
圖書封面Titlebook: Accelerating Science and Engineering Discoveries Through Integrated Research Infrastructure for Expe; 22nd Smoky Mountains Kothe Doug,Geist
影響因子This book constitutes the refereed proceedings of the 22nd Smoky Mountains Computational Sciences and Engineering Conference on Accelerating Science and Engineering Discoveries Through Integrated Research Infrastructure for Experiment, Big Data, Modeling and Simulation, SMC 2022, held virtually, during August 23–25, 2022.?.The 24 full papers included in this book were carefully reviewed and selected from 74 submissions. They were organized in topical sections as follows:?foundational methods enabling science in an integrated ecosystem; science and engineering applications requiring and motivating an integrated ecosystem; systems and software advances enabling an integrated science and engineering ecosystem; deploying advanced technologies for an integrated science and engineering ecosystem; and scientific data challenges. .
Pindex Conference proceedings 2022
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Role of Astrocytes in the Neurogenic Nichescludes the intelligence to optimize data gradation, pipeline configuration, and compute performance. The work addresses several challenges: prioritizing data to reduce movement, tracking lineage to optimize complex ML pipelines, and enabling reproducibility and portability of data selection and ML m
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Principles of Astrogliopathologyo sample the phase space needed to obtain physical observables as function of temperature. DFT calculations can provide accurate evaluations of the energies, but they are too computationally expensive for routine simulations. To circumvent this problem, machine-learning (ML) based surrogate models h
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https://doi.org/10.1007/978-1-4614-5468-7. These computing-instrument ecosystems are expected to support complex workflows that incorporate remote, automated AI-driven science experiments. Their realization, however, requires various designs to be explored and software components to be developed, in order to support the orchestration of di
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