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Irradiation of a spin-active nucleus causes an increase or decrease in the signal intensity of neighboring nuclei that are not necessarily chemically bonded or involved in J-coupling.  This phenomenon, called the Nuclear Overhauser Enhancement (NOE), results from through-space interactions between the nuclear spins. The NOE effect decreases with increasing internuclear distance and is generally not observed beyond 4 angstroms. In NOE, dipole-dipole interactions between neighboring...
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Controlled nuclear fission reactions are used to generate electricity. Any nuclear reactor that produces power via the fission of uranium or plutonium by bombardment with neutrons has six components: nuclear fuel consisting of fissionable material, a nuclear moderator, a neutron source, control rods, reactor coolant, and a shield and containment system.
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Mechanistic models play a crucial role in algorithms for numerical problem-solving, particularly in nonlinear mixed effects modeling (NMEM). These models aim to minimize specific objective functions by evaluating various parameter estimates, leading to the development of systematic algorithms. In some cases, linearization techniques approximate the model using linear equations.
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FECSG-ML:使用机器学习生成核反应截面的特征工程.

Changsong Jin1, Tiejun Li1, Jianmin Zhang1

  • 1College of Computer, National University of Defense Technology, Changsha, 410073, China.

Applied radiation and isotopes : including data, instrumentation and methods for use in agriculture, industry and medicine
|October 19, 2024
PubMed
概括

机器学习生成核截面数据,克服了EXFOR等核实验数据库的局限性. 这种新的方法使用转移学习进行准确的预测,增强核科学应用.

关键词:
截面 截面 截面 截面欧洲国家基金会 (ENDF)出口出口出口出口出口功能工程的特点工程.机器学习是机器学习.开放MCMC 开放MC

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科学领域:

  • 核科学与工程 核科学与工程
  • 计算物理和数据科学.

背景情况:

  • 准确的核数据,包括横截面,对于核反应堆的设计和安全至关重要.
  • 像EXFOR这样的现有核数据库存在局限性,因为实验数据稀缺,不一致或错误,需要手动评估.
  • 手动评估核数据容易产生偏见和不确定性.

研究的目的:

  • 开发一个机器学习框架 (FECSG-ML) 来生成核反应截面数据.
  • 为核数据库的手动评估提供数据驱动的替代方案.
  • 提高核截面数据的准确性和可靠性,用于科学应用.

主要方法:

  • 通过对ENDF/B-VIII.0数据集进行预训练和对EXFOR数据库进行微调,利用转移学习.
  • 使用机器学习将离散截面数据转换为各种同位素的连续格式.
  • 集成集体学习以优化多个截面数据集的预测.

主要成果:

  • FECSG-ML模型表现出高精度,生成的回归曲线与EXFOR数据点密切匹配.
  • 该模型在准确性方面表现优于ENDF/B-VIII.0评估数据库.
  • 生成的核截面数据已成功应用于用于针燃料组件和CANDU反应堆的OpenMC模拟.

结论:

  • FECSG-ML框架提供了一种可靠的方法来生成可靠的核截面数据.
  • 这种方法有效地取代了传统的,可能有偏见的核数据库评估过程.
  • 该研究强调了机器学习的潜力,通过改进数据生成来推进核科学和工程.