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 for installation.
Path of RETICOLO should be added in the main.py
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
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
If you use this code for your research, please cite:
Simulator-based training of generative models for the inverse design of metasurfaces.
Jiaqi Jiang, Jonathan A. Fan
Global Optimization of Dielectric Metasurfaces Using a Physics-Driven Neural Network.
Jiaqi Jiang, Jonathan A. Fan
