Presentation
18 June 2024 Machine learning applications to nonlinear fiber-optics systems
Author Affiliations +
Abstract
We will review our work in the field of smart ultrafast photonics where machine-learning algorithms are combined with nonlinear optical systems allowing for optimized performance and control. In particular, we will show how the techniques of machine learning can be efficiently exploited for the analysis of nonlinear instabilities; the prediction of complex supercontinuum generation dynamics with orders of magnitude increased computation speed when compared to conventional direct numerical integration; the optimized and precise control of the spectrum of broadband supercontinuum sources.
Conference Presentation
© (2024) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.
Goëry Genty "Machine learning applications to nonlinear fiber-optics systems", Proc. SPIE PC13017, Machine Learning in Photonics, PC1301703 (18 June 2024); https://doi.org/10.1117/12.3017628
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KEYWORDS
Complex systems

Machine learning

Fiber optics

Nonlinear optimization

Supercontinuum generation

Photonics

Solitons

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