BEGIN:VCALENDAR
VERSION:2.0
PRODID:-//Department of Electrical and Computer Engineering (HKUECE) 電機與計算機工程系 - ECPv6.16.0//NONSGML v1.0//EN
CALSCALE:GREGORIAN
METHOD:PUBLISH
X-ORIGINAL-URL:https://ece.hku.hk
X-WR-CALDESC:Events for Department of Electrical and Computer Engineering (HKUECE) 電機與計算機工程系
REFRESH-INTERVAL;VALUE=DURATION:PT1H
X-Robots-Tag:noindex
X-PUBLISHED-TTL:PT1H
BEGIN:VTIMEZONE
TZID:Asia/Hong_Kong
BEGIN:STANDARD
TZOFFSETFROM:+0800
TZOFFSETTO:+0800
TZNAME:HKT
DTSTART:20220101T000000
END:STANDARD
END:VTIMEZONE
BEGIN:VEVENT
DTSTART;VALUE=DATE:20231101
DTEND;VALUE=DATE:20231102
DTSTAMP:20260513T083041
CREATED:20231030T031257Z
LAST-MODIFIED:20250114T080259Z
UID:17774-1698796800-1698883199@ece.hku.hk
SUMMARY:Machine learning applications to nonlinear pulse propagation dynamics
DESCRIPTION:The propagation of short and intense laser pulses in an optical fiber is known to be associated with a rich landscape of nonlinear propagation scenarios and multidimensional dynamical regimes. For example\, in the coherent regime\, soliton dynamics can lead to the generation of a broadband supercontinuum while in the incoherent regime noise amplification can lead to the development of instabilities that have been associated with the emergence of extreme events. These can be challenging to model and control using conventional approaches. Recently\, there has been rapid growth in the field of smart ultrafast photonics where machine-learning algorithms are combined with nonlinear optical systems allowing for optimized performance and control\, high-speed characterization and identification of particular features within noisy data\, or enhanced functionalities. In this talk\, we will review our work in this area and\, in particular\, we will show how the techniques of machine learning can be efficiently exploited for the analysis of nonlinear instabilities and rogue waves; the prediction of complex supercontinuum generation dynamics with orders of magnitude increased computation speed when compared to conventional direct numerical integration of the generalized nonlinear Schrödinger equation; the optimized and precise control of the spectrum of broadband supercontinuum sources for spectroscopic applications. \nBiography of the speaker: \nGoëry Genty obtained his from Ecole Supérieure d’Optique (France) in 1998 and PhD degree from Aalto University (Finland) in 2004. He has been Professor at Tampere University since 2014. His interest ranges from the study of ultrafast dynamics and instabilities\, supercontinuum generation\, to multimode systems\, real-time measurement techniques\, and machine learning. Awards include the IUPAP Young Scientist International Prize in Optics in 2011 and the Physics Prize of Finnish Academy of Science and Letters in 2019. He is Fellow of the Optical Society of America and European Optical Society and has published more than 150 publications in peer-reviewed journals. Goëry Genty is also the director of the Flagship for Photonics Research and Innovation and director of the Finnish national research infrastructure for light-based technologies\, two of the most prestigious research programs funded by the Research Council of Finland.
URL:https://ece.hku.hk/events/machine-learning-applications-to-nonlinear-pulse-propagation-dynamics/
CATEGORIES:Seminar
ATTACH;FMTTYPE=image/jpeg:https://ece.hku.hk/wp-content/uploads/2024/02/Seminar-s-banner.jpg
END:VEVENT
END:VCALENDAR