Machine Learning Analysis of Optical Rogue Solitons in Supercontinuum Generation

Lauri Salmela; Coraline Lapre; John M. Dudley; Goery Genty

Machine Learning Analysis of Optical Rogue Solitons in Supercontinuum Generation

Lauri Salmela
, Coraline Lapre
, John M. Dudley
, Goery Genty

Research output: Other conference contribution › Abstract › Scientific

Abstract

We use machine learning techniques to predict the peak intensity and temporal shift of extreme red-shifted rogue solitons in supercontinuum generation from simulated single-shot spectral intensity profiles without any phase information.

Original language	English
Publication status	Published - May 2020
Publication type	Not Eligible
Event	2020 Conference on Lasers and Electro-Optics, CLEO 2020 - San Jose, United States Duration: 10 May 2020 → 15 May 2020

Conference

Conference	2020 Conference on Lasers and Electro-Optics, CLEO 2020
Country/Territory	United States
City	San Jose
Period	10/05/20 → 15/05/20

ASJC Scopus subject areas

Electronic, Optical and Magnetic Materials
Electrical and Electronic Engineering

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https://ieeexplore.ieee.org/document/9193308

Cite this

@conference{804bc7d8ed7148708b56770f574c57ea,

title = "Machine Learning Analysis of Optical Rogue Solitons in Supercontinuum Generation",

abstract = "We use machine learning techniques to predict the peak intensity and temporal shift of extreme red-shifted rogue solitons in supercontinuum generation from simulated single-shot spectral intensity profiles without any phase information.",

author = "Lauri Salmela and Coraline Lapre and Dudley, \{John M.\} and Goery Genty",

note = "Publisher Copyright: {\textcopyright} 2020 OSA.; 2020 Conference on Lasers and Electro-Optics, CLEO 2020 ; Conference date: 10-05-2020 Through 15-05-2020",

year = "2020",

month = may,

language = "English",

}

TY - CONF

T1 - Machine Learning Analysis of Optical Rogue Solitons in Supercontinuum Generation

AU - Salmela, Lauri

AU - Lapre, Coraline

AU - Dudley, John M.

AU - Genty, Goery

PY - 2020/5

Y1 - 2020/5

N2 - We use machine learning techniques to predict the peak intensity and temporal shift of extreme red-shifted rogue solitons in supercontinuum generation from simulated single-shot spectral intensity profiles without any phase information.

AB - We use machine learning techniques to predict the peak intensity and temporal shift of extreme red-shifted rogue solitons in supercontinuum generation from simulated single-shot spectral intensity profiles without any phase information.

M3 - Abstract

AN - SCOPUS:85091645283

T2 - 2020 Conference on Lasers and Electro-Optics, CLEO 2020

Y2 - 10 May 2020 through 15 May 2020

ER -

Machine Learning Analysis of Optical Rogue Solitons in Supercontinuum Generation

Abstract

Conference

ASJC Scopus subject areas

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Cite this