Data download

1. Download Data

• download by link

  download and unzip under folder named 'data'

  data.zip

• download by shell

wget https://drive.google.com/uc?id=1Gs9Per_unwdmlXufDxmYEgLAve0ep8Xx -O data.zip
unzip -d data data.zip

Conda Setting

curl -O https://repo.anaconda.com/archive/Anaconda3-2019.10-Linux-x86_64.sh
sha256sum Anaconda3-2019.10-Linux-x86_64.sh
bash Anaconda3-2019.10-Linux-x86_64.sh

conda create -n maxwellfdfd-controlgan python=3.10
conda activate maxwellfdfd-controlgan
pip install -r requirements.txt

Run script

• Train

  • wgan

  python wgan.py 

  • conditional gan (cgan)

  python cgan.py

  • controllable gan (controlgan)

  python controlgan.py

  • simulator loss (simgan)

  # train simulator model    
  python train_cnn_torch.ipynb
  # train gan
  python train_simGAN.py

• Test

  python result_test.py 

• Sample image to percent match, truth csv

  python result_box_plot.py -fn ./logs/wgan

To generate the data please visit the following GitHub URL :

https://github.com/wonderit/maxwellfdfd

Reference

• https://github.com/BrainJellyPie/ControlGAN
• https://github.com/igul222/improved_wgan_training
• https://github.com/wonderit/maxwellfdfd-ai
• https://github.com/wonderit/ControlGAN
• Kim, Wonsuk, and Junhee Seok. "Simulation acceleration for transmittance of electromagnetic waves in 2D slit arrays using deep learning." Scientific reports 10.1 (2020): 1-8.
• Lee, Minhyeok, and Junhee Seok. "Controllable generative adversarial network." Ieee Access 7 (2019): 28158-28169.
• Liu, Zhaocheng, et al. "Generative model for the inverse design of metasurfaces." Nano letters 18.10 (2018): 6570-6576.