https://github.com/jonfanlab/GLOnet

Global optimization based on generative neural networks
https://github.com/jonfanlab/GLOnet

Last synced: 2 months ago
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Global optimization based on generative neural networks

• Host: GitHub
• URL: https://github.com/jonfanlab/GLOnet
• Owner: jonfanlab
• License: mit
• Created: 2019-05-23T12:15:07.000Z (about 5 years ago)
• Default Branch: master
• Last Pushed: 2022-11-17T07:11:09.000Z (over 1 year ago)
• Last Synced: 2024-01-24T22:04:54.801Z (5 months ago)
• Language: Python
• Homepage:
• Size: 531 KB
• Stars: 94
• Watchers: 6
• Forks: 38
• Open Issues: 1
• Metadata Files:
  • Readme: README.md
  • License: LICENSE

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• awesome_photonics - glonet: global optimization based on generative neural networks

README

        
# Global optimization based on generative nerual networks (GLOnet)

## Requirements

We recommend using python3 and a virtual environment

```

virtualenv -p python3 .env

source .env/bin/activate

pip install -r requirements.txt

```

When you're done working on the project, deactivate the virtual environment with `deactivate`.

A matlab engine for python is needed for EM simulation. Please refer to [MathWorks Pages](https://www.mathworks.com/help/matlab/matlab_external/install-matlab-engine-api-for-python-in-nondefault-locations.html) for installation.

Path of [RETICOLO](https://www.lp2n.institutoptique.fr/equipes-de-recherche-du-lp2n/light-complex-nanostructures) should be added in the `main.py`

## Training the GLOnet

You can change the parameters by editing `Params.json` in `results` folder. 

If you want to train the network, simply run

```

python main.py 

```

or 

```

python main.py --output_dir results --wavelength 900 --angle 60

```

to specify non-default output folder or parameters

## Results

All results will store in output_dir/ folder.

	

	-figures/  (figures of generated devices and loss function curve)

	

	-model/    (all weights of the generator)

	

	-outputs/  (500 generated devices in `.mat` format)

	

	-history.mat

	

	-train.log

## Citation

If you use this code for your research, please cite:

[Simulator-based training of generative models for the inverse design of metasurfaces.
](https://arxiv.org/abs/1906.07843)

Jiaqi Jiang, Jonathan A. Fan 

[Global Optimization of Dielectric Metasurfaces Using a Physics-Driven Neural Network.
](https://pubs.acs.org/doi/abs/10.1021/acs.nanolett.9b01857)

Jiaqi Jiang, Jonathan A. Fan