/MetaLab

Learn and design nanophotonic structures, surface plasmon devices... Using powerful machine learning algorithms(CNN, GBRT, differentiable forest...)

Primary LanguagePython

MetaLab

MetaLab is an open-source Python/C++ programming package for the inverse design of nanoscale optics devices by machine learning methods. By using powerful machine learning algorithms (deep neural network, GBDT ...), MetaLab supports quick design of optical structures with high accuracy. The latest version could be accessed and downloaded from https://github.com/closest-git/MetaLab.

Work with us

We’re especially interested in hearing from and potentially working with those who are studying

  • Inverse design of nanophotonic devices by machine learning methods
  • Novel machine learning methods for inverse problems

Citation

Please use the following bibtex entry:

@article{chen2019smart,
  title={Smart inverse design of graphene-based photonic metamaterials by an adaptive artificial neural network},
  author={Chen, Yingshi and Zhu, Jinfeng and Xie, Yinong and Feng, Naixing and Liu, Qing Huo},
  journal={Nanoscale},
  volume={11},
  number={19},
  pages={9749--9755},
  year={2019},
  publisher={Royal Society of Chemistry}
}

Future work

We may release code for more models.

License

MIT

Authors

MetaLAB was written by Yingshi Chen(gsp.cys@gmail.com).