Deep reinforcement learning empowers automated inverse design and optimization of photonic crystals...
Deep reinforcement learning empowers automated inverse design and optimization of photonic crystals for nanoscale laser cavities
About this item
Full title
Author / Creator
Li, Renjie , Zhang, Ceyao , Xie, Wentao , Gong, Yuanhao , Ding, Feilong , Dai, Hui , Chen, Zihan , Yin, Feng and Zhang, Zhaoyu
Publisher
Germany: De Gruyter
Journal title
Language
English
Formats
Publication information
Publisher
Germany: De Gruyter
Subjects
More information
Scope and Contents
Contents
Photonics inverse design relies on human experts to search for a design topology that satisfies certain optical specifications with their experience and intuitions, which is relatively labor-intensive, slow, and sub-optimal. Machine learning has emerged as a powerful tool to automate this inverse design process. However, supervised or semi-supervis...
Alternative Titles
Full title
Deep reinforcement learning empowers automated inverse design and optimization of photonic crystals for nanoscale laser cavities
Authors, Artists and Contributors
Identifiers
Primary Identifiers
Record Identifier
TN_cdi_doaj_primary_oai_doaj_org_article_85a90c81f6474747af2673bf2a1d29fe
Permalink
https://devfeature-collection.sl.nsw.gov.au/record/TN_cdi_doaj_primary_oai_doaj_org_article_85a90c81f6474747af2673bf2a1d29fe
Other Identifiers
ISSN
2192-8614,2192-8606
E-ISSN
2192-8614
DOI
10.1515/nanoph-2022-0692
