缩小序列模型用于对过堆谱仪的响应矩阵进行近似估计
C-S Wong1, S V Luedtke1, D P Broughton1
1Los Alamos National Laboratory, Los Alamos, New Mexico 87545, USA.
The Review of scientific instruments
|August 8, 2024
概括
一个新的减少顺序模型快速计算过堆光谱仪 (FSS) 的响应矩阵. 这使得模块化矩阵构建能够使用机器学习进行高效的FSS设计优化.
科学领域:
- 频谱学是一种光谱学.
- 计算物理 计算物理
- 材料科学 材料科学 材料科学
背景情况:
- 过堆光谱仪 (FSS) 对于各种分析应用至关重要.
- 准确的响应矩阵对于FSS性能至关重要,但需要大量的计算才能生成.
- 优化FSS设计需要快速评估众多配置.
研究的目的:
- 在FSS中引入一个减少顺序模型 (ROM) 用于加速响应矩阵计算.
- 从预先计算的物理数据中实现响应矩阵的模块化构建.
- 为FSS设计优化提供一个可行的前模型,特别是通过机器学习.
主要方法:
- 开发了一种减少顺序建模方法.
- 使用预先计算的光子和电子传输/散射数据实现模块化响应矩阵生成.
- 整合了ROM作为设计优化研究的前模型.
主要成果:
- ROM显著加快了FSS的响应矩阵计算速度.
- 模块化矩阵适用于设计优化,而不是高可靠性数据分析.
- 该模型有助于快速采样和测试各种FSS设计.
结论:
- 减少顺序模型为FSS响应矩阵生成提供了一种有效的方法.
- 这种方法特别有利于机器学习驱动的FSS设计优化.
- 模块化增强了探索各种过器和探测器材料的灵活性.
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