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5 March 2021 Deep learning for new insights into ultrafast dynamics and extreme events in nonlinear fibre optics
Goery Genty, Lauri Salmela, Alessandro Foi, Juha Toivonen, Mathilde Hary, Mikko Narhi, Medhdi Mabed, Cyril Billet, John M. Dudley
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Proceedings Volume 11703, AI and Optical Data Sciences II; 1170316 (2021) https://doi.org/10.1117/12.2576979
Event: SPIE OPTO, 2021, Online Only
Abstract
Although the successes of artificial intelligence in areas such as automatic translation are well known, the application of the powerful techniques of deep learning to current optics research is at a comparatively early stage. However, an area with particular promise for deep learning to accelerate both basic science and applications is in ultrafast optics, where nonlinear light-matter interactions lead to highly complex dynamics, including the emergence of extreme events. In the particular field of nonlinear fibre optics, we have recently reported a number of results that have shown how deep learning can both augment existing experimental techniques as well as provide new theoretical insights into the underlying physics. The objective of this paper is to review a selection of our work in this area.
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Goery Genty, Lauri Salmela, Alessandro Foi, Juha Toivonen, Mathilde Hary, Mikko Narhi, Medhdi Mabed, Cyril Billet, and John M. Dudley "Deep learning for new insights into ultrafast dynamics and extreme events in nonlinear fibre optics", Proc. SPIE 11703, AI and Optical Data Sciences II, 1170316 (5 March 2021); https://doi.org/10.1117/12.2576979
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KEYWORDS
Nonlinear optics
Fiber optics
Ultrafast phenomena
Neural networks
Solitons
Modulation
Numerical integration
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