AI 中文总结
该研究提出物理信息遗传算法（PIGAs），将LIBS强度与冲击波特性的物理约束融入遗传算法优化，可有效降低LIBS谱线相对标准偏差，性能优于传统归一化方法，还可扩展至其他低温等离子体领域。
AI 中文摘要
受物理信息神经网络（PINNs）兼具物理定律可解释性与机器学习高效集成能力的启发，我们提出一种基于化学计量烧蚀的LIBS光谱归一化框架，将LIBS强度与冲击波特性（温度Tshock和压力P）之间的物理约束编码为具有多个独立目标的优化算法，命名为物理信息遗传算法（PIGAs）。该算法适用于涵盖激光诱导击穿至显著等离子体屏蔽的更宽激光能量范围，以及经历自吸收的谱线，其性能优于广泛使用的物理线性或多变量数据驱动归一化方法。自制的端到端LAP-RTE代码作为基准，验证了PIGAs的物理倒数对数变换及其对自吸收谱线的扩展性。后续实验谱线经统计验证PIGAs的校正效果：108条Fe I谱线的强度中位数相对标准偏差（RSD）经P校正后有效降低85%，经Tshock校正后降低88%；33条Fe II谱线经P校正后降低77%，经Tshock校正后降低86%；17条自吸收谱线也得到有效校正，RSD经P校正后降低78%，经Tshock校正后降低89%。我们提出的将优化方法结合以量化归一化策略中未知参数的思路，还可扩展用于挖掘其他具有类似过程的低温等离子体领域中参数间的相关性。
英文摘要
Inspired by physics-informed neural networks (PINNs) inheriting both the interpretability of physical laws and the efficient integration capability of machine learning, we propose a framework based on stoichiometric ablation for LIBS spectral normalization, encoding physical constraints between LIBS intensities and shockwave characteristics (temperature Tshock and pressure P) into optimization algorithms with multiple independent objectives, named physics-informed genetic algorithms (PIGAs). It is characterized by its applicability to the wider laser energy range covering laser-induced breakdown to significant plasma shielding and spectral lines undergoing self-absorption outperforming the widely-used physical linear or multivariate data-driven normalization methods. The home-made end-to-end LAP-RTE codes serves as the benchmark to validate the physical reciprocal-logarithmic transformation and its extensibility to self-absorption spectral lines for PIGAs. Next experimental spectral lines are statistically used to validate PIGAs correction effects, the median RSDs of spectral intensities can be effectively reduced by 85% (corrected by P) and 88% (corrected by Tshock) for 108 Fe I lines, while for 33 Fe II lines, reduced by 77% (corrected by P) and 86% (corrected by Tshock). Seventeen self-absorption lines are also corrected effectively, with RSDs being reduced by 78% (corrected by P) and 89% (corrected by Tshock). Our proposed idea of combining optimization methods to quantify unknown parameters in normalization strategies can also be extended to excavate the correlation between parameters for other low-temperature plasma fields with similar processes.
CommentsPublished in Applied Physics Letters. This is the authors' version. 6 pages, 5 figures
Journal refY. Zhou, J. Wu, M. Shi, M. Chen, J. Li, X. Guo, Y. Hang, C. Pei, and X. Li, Appl. Phys. Lett. 126 (3), 034103 (2025)
