The field of machine learning potentially brings a new set of powerful tools to optical communications and photonics. However, to separate hype from reality it is vital that such tools are evaluated properly and used judiciously.
This is a preview of subscription content, access via your institution
Relevant articles
Open Access articles citing this article.
-
Meshless optical mode solving using scalable deep deconvolutional neural network
Scientific Reports Open Access 19 January 2023
-
An adaptive approach to machine learning for compact particle accelerators
Scientific Reports Open Access 28 September 2021
-
Multipurpose self-configuration of programmable photonic circuits
Nature Communications Open Access 11 December 2020
Access options
Access Nature and 54 other Nature Portfolio journals
Get Nature+, our best-value online-access subscription
$29.99 / 30 days
cancel any time
Subscribe to this journal
Receive 12 print issues and online access
$209.00 per year
only $17.42 per issue
Buy this article
- Purchase on Springer Link
- Instant access to full article PDF
Prices may be subject to local taxes which are calculated during checkout
Author information
Authors and Affiliations
Corresponding authors
Rights and permissions
About this article
Cite this article
Zibar, D., Wymeersch, H. & Lyubomirsky, I. Machine learning under the spotlight. Nature Photon 11, 749–751 (2017). https://doi.org/10.1038/s41566-017-0058-3
Published:
Issue Date:
DOI: https://doi.org/10.1038/s41566-017-0058-3
This article is cited by
-
Meshless optical mode solving using scalable deep deconvolutional neural network
Scientific Reports (2023)
-
Machine learning and applications in ultrafast photonics
Nature Photonics (2021)
-
An adaptive approach to machine learning for compact particle accelerators
Scientific Reports (2021)
-
Analogue computing with metamaterials
Nature Reviews Materials (2020)
-
Multipurpose self-configuration of programmable photonic circuits
Nature Communications (2020)