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| DOI | Trouver le DOI : https://doi.org/10.1038/s41598-025-16408-4 |
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| Auteur | Rechercher : Hunter, Robert F. H.; Rechercher : Forcade, Gavin P.; Rechercher : Grinberg, Yuri1Identifiant ORCID : https://orcid.org/0000-0003-3349-1590; Rechercher : Wilson, D. Paige; Rechercher : Beattie, Meghan N.; Rechercher : Valdivia, Christopher E.; Rechercher : de Lafontaine, Mathieu; Rechercher : St-Arnaud, Louis-Philippe; Rechercher : Helmers, Henning; Rechercher : Höhn, Oliver; Rechercher : Lackner, David; Rechercher : Pellegrino, Carmine; Rechercher : Krich, Jacob J.; Rechercher : Walker, Alexandre W.2Identifiant ORCID : https://orcid.org/0000-0002-1791-2140; Rechercher : Hinzer, Karin |
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| Affiliation | - Conseil national de recherches Canada. Technologies numériques
- Conseil national de recherches Canada. Quantique et nanotechnologies
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| Bailleur de fonds | Rechercher : National Research Council Canada; Rechercher : National Sciences and Engineering Research Council of Canada; Rechercher : The Canadian Foundation for Innovation; Rechercher : The Government of Ontario; Rechercher : The German Federal Ministry of Education and Research; Rechercher : ERC grant PHASE |
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| Format | Texte, Article |
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| Sujet | machine learning; dimensionality reduction; design discovery; optimization acceleration; knowledge discovery; multi-junction photonic power converters |
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| Résumé | Machine learning is proving to be a revolutionary tool across many disciplines, including optoelectronic device design. In this report, we compare classical and machine learning enhanced design optimization methodologies. We investigate, as an example case, the design of the complex structures of tenjunction InP lattice matched photonic power converters with In₀.₅₃Ga₀.₄₇As absorbers optimized for operation at 1550 nm. We find that the implicit pattern recognition capabilities of dimensionality reduction using principal component analysis accelerate design discovery, optimization, and the understanding of complex optical phenomena in the simulated devices. The dimensionality reduction approach offers over twenty times as many optimal designs with greater variability and with a 15% reduction in computational cost compared to a classical optimization method. Furthermore, we find that the representation of the reduced dimensionality subspace offers an intuitive interpretation of optical phenomena expected to occur in this design problem. This method is general and offers the potential for knowledge discovery, expanded design perspective, and optimization acceleration in conjunction with a significant reduction in computational expense in systems which can be numerically modeled. |
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| Date de publication | 2025-09-26 |
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| Maison d’édition | Springer Nature |
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| Licence | |
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| Dans | |
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| Langue | anglais |
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| Publications évaluées par des pairs | Oui |
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| Exporter la notice | Exporter en format RIS |
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| Signaler une correction | Signaler une correction (s'ouvre dans un nouvel onglet) |
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| Identificateur de l’enregistrement | 0a590f7b-cb4f-4bd3-bbb5-7ac3afd1d19a |
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| Enregistrement créé | 2025-10-08 |
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| Enregistrement modifié | 2025-11-03 |
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