| Autor: |
Daniele Melati, Yuri Grinberg, Mohsen Kamandar Dezfouli, Siegfried Janz, Pavel Cheben, Jens H. Schmid, Alejandro Sánchez-Postigo, Dan-Xia Xu |
| Jazyk: |
angličtina |
| Rok vydání: |
2019 |
| Předmět: |
|
| Zdroj: |
Nature Communications, Vol 10, Iss 1, Pp 1-9 (2019) |
| Druh dokumentu: |
article |
| ISSN: |
2041-1723 |
| DOI: |
10.1038/s41467-019-12698-1 |
| Popis: |
Machine learning is increasingly used in nanophotonics for designing novel classes of complex devices but the general parameter behavior is often neglected. Here, the authors report a new methodology to discover and visualize optimal design spaces with respect to multiple performance objectives. |
| Databáze: |
Directory of Open Access Journals |
| Externí odkaz: |
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