Machine learning applied to inverse systems design

Uiara C. De Moura; Francesco Da Ros; Darko Zibar; Ann Margareth Rosa Brusin; Andrea Carena

doi:10.23919/ONDM54585.2022.9782836

Machine learning applied to inverse systems design

Uiara C. De Moura, Francesco Da Ros, Darko Zibar, Ann Margareth Rosa Brusin, Andrea Carena

Polytechnic University of Turin

Research output: Chapter in Book/Report/Conference proceeding › Article in proceedings › Research › peer-review

Abstract

In this work, we will give an overview of some of the most recent and successful applications of machine learning-based inverse system designs in photonic systems. Then, we will focus on our recent research on the Raman amplifier inverse design. We will show how the machine learning framework is optimized to generate on-demand arbitrary Raman gain profiles in a controlled and fast way and how it can become a key feature for future optical communication systems.

Original language	English
Title of host publication	Proceedings of 2022 International Conference on Optical Network Design and Modeling
Number of pages	3
Publisher	IEEE
Publication date	2022
ISBN (Print)	978-1-6654-7980-6
DOIs	https://doi.org/10.23919/ONDM54585.2022.9782836
Publication status	Published - 2022
Event	2022 International Conference on Optical Network Design and Modeling - Warsaw, Poland Duration: 16 May 2022 → 19 May 2022

Conference

Conference	2022 International Conference on Optical Network Design and Modeling
Country/Territory	Poland
City	Warsaw
Period	16/05/2022 → 19/05/2022

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10.23919/ONDM54585.2022.9782836

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title = "Machine learning applied to inverse systems design",

abstract = "In this work, we will give an overview of some of the most recent and successful applications of machine learning-based inverse system designs in photonic systems. Then, we will focus on our recent research on the Raman amplifier inverse design. We will show how the machine learning framework is optimized to generate on-demand arbitrary Raman gain profiles in a controlled and fast way and how it can become a key feature for future optical communication systems.",

author = "{De Moura}, {Uiara C.} and {Da Ros}, Francesco and Darko Zibar and Brusin, {Ann Margareth Rosa} and Andrea Carena",

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booktitle = "Proceedings of 2022 International Conference on Optical Network Design and Modeling",

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note = "2022 International Conference on Optical Network Design and Modeling , ONDM 2022 ; Conference date: 16-05-2022 Through 19-05-2022",

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AU - De Moura, Uiara C.

AU - Da Ros, Francesco

AU - Zibar, Darko

AU - Brusin, Ann Margareth Rosa

AU - Carena, Andrea

PY - 2022

Y1 - 2022

N2 - In this work, we will give an overview of some of the most recent and successful applications of machine learning-based inverse system designs in photonic systems. Then, we will focus on our recent research on the Raman amplifier inverse design. We will show how the machine learning framework is optimized to generate on-demand arbitrary Raman gain profiles in a controlled and fast way and how it can become a key feature for future optical communication systems.

AB - In this work, we will give an overview of some of the most recent and successful applications of machine learning-based inverse system designs in photonic systems. Then, we will focus on our recent research on the Raman amplifier inverse design. We will show how the machine learning framework is optimized to generate on-demand arbitrary Raman gain profiles in a controlled and fast way and how it can become a key feature for future optical communication systems.

U2 - 10.23919/ONDM54585.2022.9782836

DO - 10.23919/ONDM54585.2022.9782836

M3 - Article in proceedings

SN - 978-1-6654-7980-6

BT - Proceedings of 2022 International Conference on Optical Network Design and Modeling

PB - IEEE

T2 - 2022 International Conference on Optical Network Design and Modeling

Y2 - 16 May 2022 through 19 May 2022

ER -

Machine learning applied to inverse systems design

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