Robotics & Machine Learning Daily News2024,Issue(Jun.19) :97-98.

Researchers at Texas A&M University Release New Data on Machine Lea rning (Traditional Machine Learning Methods In Predicting the Physics of Subcrit ical Systems In Source-equilibrium)

德克萨斯农工大学的研究人员发布了关于机器学习(传统机器学习方法在预测源平衡状态下子系统的物理)的新数据

Robotics & Machine Learning Daily News2024,Issue(Jun.19) :97-98.

Researchers at Texas A&M University Release New Data on Machine Lea rning (Traditional Machine Learning Methods In Predicting the Physics of Subcrit ical Systems In Source-equilibrium)

德克萨斯农工大学的研究人员发布了关于机器学习(传统机器学习方法在预测源平衡状态下子系统的物理)的新数据

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摘要

由一名新闻记者-机器人与机器学习的工作人员新闻编辑每日新闻-调查人员讨论机器学习的新发现。根据NewsRx记者从德克萨斯州大学站发回的新闻报道,研究人员称,“反应性测量方法S,即将积分物理量与系统可观测值联系起来的公式,我们主要从点反应器动力学(PRK)导出。PRK假定的基本模式仅由裂变驱动;然而,在亚临界组件(SCA S)中,存在一个独立的源来维持通量。”这项研究的财政支助来自菲律宾共和国科学和技术部(DOST)科学教育研究所。

Abstract

By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News Daily News-Investigators discuss new findings in Machine Learning. According to news reporting originating from College Station, Texas, by NewsRx correspondents, research stated, "Reactivity measurement method s, that are formulas relating integral physics quantity to system observable, we re mostly derived from Point Reactor Kinetics (PRK). PRK presupposes fundamental mode that is driven solely by fissions; however, in Subcritical Assemblies (SCA s), an independent source is present to maintain flux." Financial support for this research came from Department of Science and Technolo gy (DOST) - Science Education Institute of the Republic of the Philippines.

Key words

College Station/Texas/United States/N orth and Central America/Cyborgs/Emerging Technologies/Machine Learning/Texa s A&M University

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出版年

2024
Robotics & Machine Learning Daily News

Robotics & Machine Learning Daily News

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