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Updated: Jan 17, 2026

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深度学习的应用在一次性气相激光诱导分解光谱学中

Nathan P Brown, Elijah R Jans, Garrett W Idler

|January 15, 2026

概括

一个贝叶斯优化的卷积神经网络 (CNN) 能够有效地解释一次性激光诱导分解光谱 (LIBS) 测量结果. 这一进步有助于分析聚变驱动器中的电极脱吸现象.

科学领域:

等离子体物理学的物理学
频谱学是一种光谱学.
机器学习机器学习

背景情况:

单射激光诱导分解光谱 (LIBS) 对于在脉冲动力聚变驱动器中诊断ns级电极脱落至关重要.
从低密度气体混合物中解释LIBS测量具有重大挑战.

研究的目的:

证明贝叶斯优化的卷积神经网络 (CNN) 的有效性,用于解释一次性LIBS测量.
分析脉冲动力聚变驱动器中的电极脱吸现象.

主要方法:

通过混合 (H2) 和甲 (CH4) 以不同的流速 (80-530 mTorr) 生成256种不同的测量条件.
采用贝叶斯优化的CNN来解释这些气体混合物的LIBS数据.
验证了CNN对H2和CH4流速的预测准确度.

主要成果:

美国有线电视新闻网 (CNN) 准确地预测了H2流速,其平方根平均误差 (RMSE) 为15.9平方厘米.
美国有线电视新闻网 (CNN) 预测了CH4流速,RMSE为12.0平方厘米.
每种气体的平均相对预测误差为<9%,保持在10%以下.

结论:

开发的CNN模型成功地解释了具有挑战性的一次性LIBS测量.
这种方法显示出在聚变能源研究中诊断电极脱落的巨大潜力.
准确的气体流速预测是可以实现的,尽管信号重叠.

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