Deep learning enabled topological design of exceptional points for multi-optical-parameter control
- DOI
- 10.1038/s42005-023-01380-0
- Published
- 2023-09-16
- Container
- Communications Physics
- Publisher
- Springer Science and Business Media LLC
- Open access
- unknown
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Cite this work
BibTeX
@article{allodium:10.1038/s42005-023-01380-0,
title = {Deep learning enabled topological design of exceptional points for multi-optical-parameter control},
author = {Peng Fu and Shuo Du and Wenze Lan and Leyong Hu and Yiqing Wu and Zhenfei Li and Xin Huang and Yang Guo and Weiren Zhu and Junjie Li and Baoli Liu and Changzhi Gu},
year = {2023},
journal = {Communications Physics},
doi = {10.1038/s42005-023-01380-0},
url = {https://doi.org/10.1038/s42005-023-01380-0}
}RIS
TY - JOUR TI - Deep learning enabled topological design of exceptional points for multi-optical-parameter control AU - Peng Fu AU - Shuo Du AU - Wenze Lan AU - Leyong Hu AU - Yiqing Wu AU - Zhenfei Li AU - Xin Huang AU - Yang Guo AU - Weiren Zhu AU - Junjie Li AU - Baoli Liu AU - Changzhi Gu PY - 2023 JO - Communications Physics DO - 10.1038/s42005-023-01380-0 UR - https://doi.org/10.1038/s42005-023-01380-0 ER -
APA
Fu, P., Du, S., Lan, W., Hu, L., Wu, Y., Li, Z., Huang, X., Guo, Y., Zhu, W., Li, J., Liu, B., & Gu, C. (2023). Deep learning enabled topological design of exceptional points for multi-optical-parameter control. Communications Physics. https://doi.org/10.1038/s42005-023-01380-0
Source records
- crossref · retrieved 2026-09-26T13:36:05.154Z