Machine learning-assisted design of inverted gradient multiring optical fibres for flat-top beam generation
Artykuł w czasopiśmie
MNiSW
140
Lista 2024
| Status: | |
| Autorzy: | Dziuba-Kozieł Marta, Kozieł Grzegorz, Markiewicz Jakub, Kochanowicz Marcin, Dorosz Dominik, Miluski Piotr, Kisała Piotr |
| Dyscypliny: | |
| Aby zobaczyć szczegóły należy się zalogować. | |
| Rok wydania: | 2026 |
| Wersja dokumentu: | Drukowana | Elektroniczna |
| Język: | angielski |
| Numer czasopisma: | 4 |
| Wolumen/Tom: | 34 |
| Strony: | 7159 - 7173 |
| Impact Factor: | 3,3 |
| Web of Science® Times Cited: | 0 |
| Scopus® Cytowania: | 0 |
| Bazy: | Web of Science | Scopus |
| Efekt badań statutowych | NIE |
| Finansowanie: | Lublin University of Technology (FD-20/IT-3/064, FD-20/IT-3/059, FD-20/EE-2/309, FD-20/EE-2/999); National Science Centre (UMO-2023/49/B/ST7/03218). |
| Materiał konferencyjny: | NIE |
| Publikacja OA: | TAK |
| Licencja: | |
| Sposób udostępnienia: | Witryna wydawcy |
| Wersja tekstu: | Ostateczna wersja opublikowana |
| Czas opublikowania: | W momencie opublikowania |
| Data opublikowania w OA: | 23 lutego 2026 |
| Abstrakty: | angielski |
| This paper introduces a new method for designing inverted gradient multilayer optical fibres to generate flat-top laser beams with specific propagation features. Using numerical simulations in OptiFibre software, over 700 three-layer optical fibre configurations were modelled, and their output beam profiles were assessed based on beam flatness, edge steepness, and full width at half maximum (FWHM). A unique set of parameters describing the refractive index profile was proposed to improve the estimation of beam properties. The random forest algorithm was employed to predict beam characteristics based on fibre geometry, and a feature importance analysis identified key parameters that influence these characteristics. The model showed high prediction accuracy, particularly when the proposed descriptors were combined with layer refractive indices. Additionally, two multi-criteria optimisation methods – trade-off and weighted objectives – were used to find optimal fibre configurations that simultaneously maximise beam flatness, edge steepness, and FWHM. The findings offer practical guidelines for the inverse design of beam-shaping optical fibres and demonstrate the potential of machine learning to accelerate fibre design by reducing dependence on lengthy simulations. |
