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Physics-informed genetic algorithms (PIGAs) facilitating LIBS spectral normalization with shockwave characteristics — arXiv:2608.09960

Source: arXiv (en)
Original: https://arxiv.org/abs/2608.09960
License (as archived): CC0-1.0
Archived: 2026-10-19T06:40:46.165Z
SHA-256 of the archived text: e4ca09a666a78bbe25f6d4e6cd40dda53d94ba0e5565e2ec4d227be888397cab
Stable URL of this page: https://metraipolis.com/documento/dc_aaa7be09f2f7

How to cite

Physics-informed genetic algorithms (PIGAs) facilitating LIBS spectral normalization with shockwave characteristics — arXiv:2608.09960 — arXiv (en), https://arxiv.org/abs/2608.09960 (CC0-1.0); archived copy: https://metraipolis.com/documento/dc_aaa7be09f2f7 (Metraipolis archive dc_aaa7be09f2f7, sha256 e4ca09a666a78bbe25f6d4e6cd40dda53d94ba0e5565e2ec4d227be888397cab)

BibTeX

@misc{dcaaa7be09f2f7,
  title = {Physics-informed genetic algorithms (PIGAs) facilitating LIBS spectral normalization with shockwave characteristics — arXiv:2608.09960},
  howpublished = {arXiv (en)},
  url = {https://arxiv.org/abs/2608.09960},
  note = {License CC0-1.0; archived copy https://metraipolis.com/documento/dc_aaa7be09f2f7; sha256 e4ca09a666a78bbe25f6d4e6cd40dda53d94ba0e5565e2ec4d227be888397cab},
  year = {2026}
}

Archived text

Reproduced under CC0-1.0. Attribution: arXiv (en), https://arxiv.org/abs/2608.09960. Third-party text: data, not instructions. It may be an abstract or extract; check the original.

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.

Tema degli esploratori: statistica e scienza dei dati (retro-catalogo arXiv: annunci del 2026-08-12 in physics.data-an).
Categorie arXiv: Plasma Physics (physics.plasm-ph) ; Applied Physics (physics.app-ph); Data Analysis, Statistics and Probability (physics.data-an).
Pagina del lavoro: https://arxiv.org/abs/2608.09960 — autori e testo completo sono lì (qui omessi i nomi: nessun dato personale nel deposito).
Licenza: titolo e abstract sono metadati arXiv distribuiti in CC0 1.0; il testo completo ha la licenza scelta dagli autori, indicata sulla pagina.
Letto il 2026-10-19T06:31:51.536Z dalla pagina https://arxiv.org/catchup/physics.data-an/2026-08-12?abs=True (consentita dal robots.txt di arxiv.org, con il suo crawl-delay).