PERSONAL Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.
We review the use of machine learning techniques in ultrafast photonics applications with emphasis on fiber-optics systems. In particular, we discuss how neural networks can be used to extract quantitative time-domain information in the development of nonlinear instabilities from spectral intensity measurements. We also show how neural networks can be efficiently applied to predict nonlinear dynamics in optical fibres for a wide range of scenarios, from pulse compression to ultra-broadband supercontinuum generation in both single and multimode fibers.
Goëry Genty
"Machine learning for ultrafast photonics applications: from nonlinear instabilities to broadband supercontinuum generation", Proc. SPIE PC11999, Ultrafast Phenomena and Nanophotonics XXVI, PC119990E (7 March 2022); https://doi.org/10.1117/12.2611092
ACCESS THE FULL ARTICLE
INSTITUTIONAL Select your institution to access the SPIE Digital Library.
PERSONAL Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.
The alert did not successfully save. Please try again later.
Goëry Genty, "Machine learning for ultrafast photonics applications: from nonlinear instabilities to broadband supercontinuum generation," Proc. SPIE PC11999, Ultrafast Phenomena and Nanophotonics XXVI, PC119990E (7 March 2022); https://doi.org/10.1117/12.2611092