Physics-informed genetic algorithms (PIGAs) facilitating LIBS spectral normalization with shockwave characteristics

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 T_shock 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 serve 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 T_shock) for 108 Fe I lines, while for 33 Fe II lines, reduced by 77% (corrected by P) and 86% (corrected by T_shock). Seventeen self-absorption lines are also corrected effectively, with RSDs being reduced by 78% (corrected by P) and 89% (corrected by T_shock). 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.