Collaborative Photonic Crystal Fiber Property Optimization: A New Paradigm for Reverse Design

Chen, S; Wang, X; Ren, S; Yang, J; Zhang, Y; Wang, G

Collaborative Photonic Crystal Fiber Property Optimization: A New Paradigm for Reverse Design

Chen, S

Wang, X Ren, S Yang, J Zhang, Y Wang, G

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Publisher:: IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
Publication Type:: Journal Article
Citation:: IEEE Photonics Technology Letters, 2023, 35, (19), pp. 1035-1038
Issue Date:: 2023-10-01

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Field	Value	Language
dc.contributor.author	Chen, S https://orcid.org/0000-0001-9992-2264
dc.contributor.author	Wang, X
dc.contributor.author	Ren, S
dc.contributor.author	Yang, J
dc.contributor.author	Zhang, Y
dc.contributor.author	Wang, G
dc.date.accessioned	2024-03-26T04:25:03Z
dc.date.available	2024-03-26T04:25:03Z
dc.date.issued	2023-10-01
dc.identifier.citation	IEEE Photonics Technology Letters, 2023, 35, (19), pp. 1035-1038
dc.identifier.issn	1041-1135
dc.identifier.issn	1941-0174
dc.identifier.uri	http://hdl.handle.net/10453/177173
dc.description.abstract	The reverse design of photonic crystal fibers (PCFs) using AI has received significant attention in recent years. To quickly optimize the fundamental optical properties of PCFs and obtain the optimal design combination, researchers have favored machine learning algorithms. However, relying solely on the knowledge provided by a few experts has limited the versatility and performance of these models. To address these issues, we propose a collaborative PCF property optimization framework as a new paradigm for reverse design. This framework allows experts from different institutions to share their design knowledge and collaboratively optimize the fundamental optical properties of PCFs without direct data interaction. Additionally, this framework can adapt to low-resourced computing devices that only use a small dataset without any data augmentation methods to achieve excellent optimization performance. Our proposed framework provides a novel and practical platform for global collaboration among research institutions for the reverse design of micro-structured fibers.
dc.language	English
dc.publisher	IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
dc.relation.ispartof	IEEE Photonics Technology Letters
dc.relation.isbasedon	10.1109/LPT.2023.3296674
dc.rights	info:eu-repo/semantics/closedAccess
dc.subject	0205 Optical Physics, 0906 Electrical and Electronic Engineering
dc.subject.classification	Optoelectronics & Photonics
dc.subject.classification	4006 Communications engineering
dc.subject.classification	5102 Atomic, molecular and optical physics
dc.title	Collaborative Photonic Crystal Fiber Property Optimization: A New Paradigm for Reverse Design
dc.type	Journal Article
utslib.citation.volume	35
utslib.for	0205 Optical Physics
utslib.for	0906 Electrical and Electronic Engineering
pubs.organisational-group	University of Technology Sydney
pubs.organisational-group	University of Technology Sydney/Faculty of Engineering and Information Technology
pubs.organisational-group	University of Technology Sydney/Faculty of Engineering and Information Technology/School of Computer Science
utslib.copyright.status	closed_access	*
dc.date.updated	2024-03-26T04:25:01Z
pubs.issue	19
pubs.publication-status	Published
pubs.volume	35
utslib.citation.issue	19

Abstract:

The reverse design of photonic crystal fibers (PCFs) using AI has received significant attention in recent years. To quickly optimize the fundamental optical properties of PCFs and obtain the optimal design combination, researchers have favored machine learning algorithms. However, relying solely on the knowledge provided by a few experts has limited the versatility and performance of these models. To address these issues, we propose a collaborative PCF property optimization framework as a new paradigm for reverse design. This framework allows experts from different institutions to share their design knowledge and collaboratively optimize the fundamental optical properties of PCFs without direct data interaction. Additionally, this framework can adapt to low-resourced computing devices that only use a small dataset without any data augmentation methods to achieve excellent optimization performance. Our proposed framework provides a novel and practical platform for global collaboration among research institutions for the reverse design of micro-structured fibers.

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http://hdl.handle.net/10453/177173