Advancing statistical learning and artificial intelligence in nanophotonics inverse design
Nanophotonics inverse design is a rapidly expanding research field whose goal is to focus users on defining complex, high-level optical functionalities while leveraging machines to search for the required material and geometry configurations in sub-wavelength structures. The journey of inverse desig...
| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
De Gruyter
2021-12-01
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| Series: | Nanophotonics |
| Subjects: | |
| Online Access: | https://doi.org/10.1515/nanoph-2021-0660 |
| _version_ | 1797789046611640320 |
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| author | Wang Qizhou Makarenko Maksim Burguete Lopez Arturo Getman Fedor Fratalocchi Andrea |
| author_facet | Wang Qizhou Makarenko Maksim Burguete Lopez Arturo Getman Fedor Fratalocchi Andrea |
| author_sort | Wang Qizhou |
| collection | DOAJ |
| description | Nanophotonics inverse design is a rapidly expanding research field whose goal is to focus users on defining complex, high-level optical functionalities while leveraging machines to search for the required material and geometry configurations in sub-wavelength structures. The journey of inverse design begins with traditional optimization tools such as topology optimization and heuristics methods, including simulated annealing, swarm optimization, and genetic algorithms. Recently, the blossoming of deep learning in various areas of data-driven science and engineering has begun to permeate nanophotonics inverse design intensely. This review discusses state-of-the-art optimizations methods, deep learning, and more recent hybrid techniques, analyzing the advantages, challenges, and perspectives of inverse design both as a science and an engineering. |
| first_indexed | 2024-03-13T01:45:12Z |
| format | Article |
| id | doaj.art-f609d058c7ec45ab846fb8dcd86c1ae0 |
| institution | Directory Open Access Journal |
| issn | 2192-8614 |
| language | English |
| last_indexed | 2024-03-13T01:45:12Z |
| publishDate | 2021-12-01 |
| publisher | De Gruyter |
| record_format | Article |
| series | Nanophotonics |
| spelling | doaj.art-f609d058c7ec45ab846fb8dcd86c1ae02023-07-03T10:20:07ZengDe GruyterNanophotonics2192-86142021-12-0111112483250510.1515/nanoph-2021-0660Advancing statistical learning and artificial intelligence in nanophotonics inverse designWang Qizhou0Makarenko Maksim1Burguete Lopez Arturo2Getman Fedor3Fratalocchi Andrea4PRIMALIGHT, Faculty of Electrical Engineering, King Abdullah University of Science and Technology (KAUST), Thuwal23955-6900, Saudi ArabiaPRIMALIGHT, Faculty of Electrical Engineering, King Abdullah University of Science and Technology (KAUST), Thuwal23955-6900, Saudi ArabiaPRIMALIGHT, Faculty of Electrical Engineering, King Abdullah University of Science and Technology (KAUST), Thuwal23955-6900, Saudi ArabiaPRIMALIGHT, Faculty of Electrical Engineering, King Abdullah University of Science and Technology (KAUST), Thuwal23955-6900, Saudi ArabiaPRIMALIGHT, Faculty of Electrical Engineering, King Abdullah University of Science and Technology (KAUST), Thuwal23955-6900, Saudi ArabiaNanophotonics inverse design is a rapidly expanding research field whose goal is to focus users on defining complex, high-level optical functionalities while leveraging machines to search for the required material and geometry configurations in sub-wavelength structures. The journey of inverse design begins with traditional optimization tools such as topology optimization and heuristics methods, including simulated annealing, swarm optimization, and genetic algorithms. Recently, the blossoming of deep learning in various areas of data-driven science and engineering has begun to permeate nanophotonics inverse design intensely. This review discusses state-of-the-art optimizations methods, deep learning, and more recent hybrid techniques, analyzing the advantages, challenges, and perspectives of inverse design both as a science and an engineering.https://doi.org/10.1515/nanoph-2021-0660deep learninginverse designmetamaterialsnanophotonicsoptimization |
| spellingShingle | Wang Qizhou Makarenko Maksim Burguete Lopez Arturo Getman Fedor Fratalocchi Andrea Advancing statistical learning and artificial intelligence in nanophotonics inverse design Nanophotonics deep learning inverse design metamaterials nanophotonics optimization |
| title | Advancing statistical learning and artificial intelligence in nanophotonics inverse design |
| title_full | Advancing statistical learning and artificial intelligence in nanophotonics inverse design |
| title_fullStr | Advancing statistical learning and artificial intelligence in nanophotonics inverse design |
| title_full_unstemmed | Advancing statistical learning and artificial intelligence in nanophotonics inverse design |
| title_short | Advancing statistical learning and artificial intelligence in nanophotonics inverse design |
| title_sort | advancing statistical learning and artificial intelligence in nanophotonics inverse design |
| topic | deep learning inverse design metamaterials nanophotonics optimization |
| url | https://doi.org/10.1515/nanoph-2021-0660 |
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