TUTCRIS - Tampereen teknillinen yliopisto

TUTCRIS

Machine learning analysis of extreme events in optical fibre modulation instability

Tutkimustuotosvertaisarvioitu

Yksityiskohdat

AlkuperäiskieliEnglanti
Sivumäärä1
JulkaisuNature Communications
Vuosikerta9
Numero1
DOI - pysyväislinkit
TilaJulkaistu - 22 marraskuuta 2018
OKM-julkaisutyyppiA1 Alkuperäisartikkeli

Tiivistelmä

A central research area in nonlinear science is the study of instabilities that drive extreme events. Unfortunately, techniques for measuring such phenomena often provide only partial characterisation. For example, real-time studies of instabilities in nonlinear optics frequently use only spectral data, limiting knowledge of associated temporal properties. Here, we show how machine learning can overcome this restriction to study time-domain properties of optical fibre modulation instability based only on spectral intensity measurements. Specifically, a supervised neural network is trained to correlate the spectral and temporal properties of modulation instability using simulations, and then applied to analyse high dynamic range experimental spectra to yield the probability distribution for the highest temporal peaks in the instability field. We also use unsupervised learning to classify noisy modulation instability spectra into subsets associated with distinct temporal dynamic structures. These results open novel perspectives in all systems exhibiting instability where direct time-domain observations are difficult.

Julkaisufoorumi-taso

Tilastokeskuksen tieteenalat

Latausten tilastot

Ei tietoja saatavilla