Machine learning analysis of extreme events in optical fibre modulation instability
Real-time characterisation of nonlinear processes in the time domain is challenging. Here, Närhi et al. show that machine learning techniques can help overcome this limitation and use them to infer time-domain properties of optical fibre modulation instability from spectral intensity measurements.
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| Autores principales: | , , , , , |
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| Formato: | article |
| Lenguaje: | EN |
| Publicado: |
Nature Portfolio
2018
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| Materias: | |
| Acceso en línea: | https://doaj.org/article/2189e5907a1e496d9adc09f2e86e99a5 |
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| Sumario: | Real-time characterisation of nonlinear processes in the time domain is challenging. Here, Närhi et al. show that machine learning techniques can help overcome this limitation and use them to infer time-domain properties of optical fibre modulation instability from spectral intensity measurements. |
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