Reliable, efficient, and scalable photonic inverse design empowered by physics‐inspired deep learning

Guocheng Shao, Tiankuang Zhou, Tao Yan, Yanchen Guo, Yun Zhao, Ruqi Huang, Lu Fang

Open source

DOI: 10.1515/nanoph-2024-0504
Published: 2025-01-27
Container: Nanophotonics
Publisher: Wiley
Open access: unknown

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Cite this work

BibTeX

@article{allodium:10.1515/nanoph-2024-0504,
  title = {Reliable, efficient, and scalable photonic inverse design empowered by physics‐inspired deep learning},
  author = {Guocheng Shao and Tiankuang Zhou and Tao Yan and Yanchen Guo and Yun Zhao and Ruqi Huang and Lu Fang},
  year = {2025},
  journal = {Nanophotonics},
  doi = {10.1515/nanoph-2024-0504},
  url = {https://doi.org/10.1515/nanoph-2024-0504}
}

RIS

TY  - JOUR
TI  - Reliable, efficient, and scalable photonic inverse design empowered by physics‐inspired deep learning
AU  - Guocheng Shao
AU  - Tiankuang Zhou
AU  - Tao Yan
AU  - Yanchen Guo
AU  - Yun Zhao
AU  - Ruqi Huang
AU  - Lu Fang
PY  - 2025
JO  - Nanophotonics
DO  - 10.1515/nanoph-2024-0504
UR  - https://doi.org/10.1515/nanoph-2024-0504
ER  -

APA

Shao, G., Zhou, T., Yan, T., Guo, Y., Zhao, Y., Huang, R., & Fang, L. (2025). Reliable, efficient, and scalable photonic inverse design empowered by physics‐inspired deep learning. Nanophotonics. https://doi.org/10.1515/nanoph-2024-0504

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crossref · retrieved 2026-09-26T00:38:20.791Z