用物理约束神经网络从光谱数据中自动提取材料参数
Physics-based AI methodology for Material Parameter Extraction from Optical Data
- 将物理规律嵌入神经网络，结合多尺度检测与优化框架
- 在太赫兹和红外频段模拟数据上表现更鲁棒、更快
- 适合工业界快速材料表征，无需人工干预
我们提出一种基于物理的神经网络方法，用于从光谱光学数据中提取材料参数。该模型融合经典优化框架与多尺度目标检测结构，重点探究将物理先验知识融入神经网络的影响。在太赫兹与红外频段的模拟透射光谱上验证并分析了其性能。相比传统基于模型的方法，本方法设计为自主、鲁棒且高效，特别适用于工业与社会应用场景。
原文摘要 · Abstract (English)
We report on a novel methodology for extracting material parameters from spectroscopic optical data using a physics-based neural network. The proposed model integrates classical optimization frameworks with a multi-scale object detection framework, specifically exploring the effect of incorporating physics into the neural network. We validate and analyze its performance on simulated transmission spectra at terahertz and infrared frequencies. Compared to traditional model-based approaches, our method is designed to be autonomous, robust, and time-efficient, making it particularly relevant for industrial and societal applications.
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