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Photonic simulation tools for highly parallel simulation and optimization of photonic circuits in time and frequency domain.

Project description

Photontorch

Photonic simulation tools for highly parallel simulation and optimization of photonic circuits in time and frequency domain.

Introduction

Photontorch features CUDA enabled simulation and optimization of photonic circuits. It leverages the deep learning framework PyTorch to view the photonic circuit as essentially a recurrent neural network. This enables the use of native PyTorch optimizers to optimize the (physical) parameters of your circuit.

Installation

Clone the photontorch repository:

git clone http://github.com/flaport/photontorch

and link it with pip:

pip install -e photontorch

Where to start

If you don't know where to start, start going through the notebooks in the examples folder.

Documentation

A basic documentation (based on docstrings) can be viewed and generated by typing [only with python 3]

python -m photontorch.documentation

in the folder that contains photontorch.

Tests

Tests can be performed by using pytest:

pytest tests

To generate a test coverage report, run

pytest tests --cov-report html --cov photontorch

Dependencies

Required

  • Python 2.7 (linux only), 3.6 or 3.7: from Anaconda [recommended]
  • numpy: conda install numpy [linear algebra]
  • pytorch>=0.4.0: conda install pytorch -c pytorch [linear algebra with backpropagation]
  • scipy: conda install scipy [for the photodetector]

Recommended

  • tqdm: conda install tqdm [progress bars]
  • pytest: conda install pytest pytest-cov [testing]
  • networkx: conda install networkx [graph visualization of network]
  • matplotlib: conda install matplotlib [results visualization of network]

Copyright

© Floris Laporte - MIT License

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