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README.md

Discovering physical concepts with neural networks

Code for: R. Iten, T. Metger, H.Wilming, L. del Rio, and R. Renner. "Discovering physical concepts with neural networks", arXiv:1807.10300 (2018).

This repository contains the trained Tensorflow models used in the paper as well as code to load, train and analyze them.

Requires:

  • Python 2.7
  • numpy
  • matplotlib
  • tensorflow
  • tensorboard
  • tqdm
  • jupyter

Branches:

  • master: Implementation of beta-VAE [1] for reference. Includes an example in the /analysis folder that shows how to set up and train a network.
  • pendulum: SciNet finds correct physical parameters describing a damped pendulum.
  • angular_momentum: SciNet finds and exploits angular momentum conservation to make predictions.
  • qubit: SciNet recovers correct number of parameters describing quantum states.
  • copernicus: SciNet discovers heliocentric model of the solar system.

To use the code:

  1. Clone the repository.
  2. Add the cloned directory nn_physical_concepts to your python path. See here for instructions for doing this in a virtual environment. Without a virtual environment, see here.
  3. Import from scinet import *. This includes the shortcuts nn to the model.py code and dl to the data_loader.py code.
  4. Import additional files (e.g. data generation scripts) using import scinet.my_data_generator as my_data_gen_name.

Generated data files are stored in the data directory. Saved models are stored in the tf_save directory. Tensorboard logs are stored in the tf_log directory.

Some documentation is available in the code. For further questions, please contact us directly.

[1] Higgins, I. et al. beta-VAE: "Learning Basic Visual Concepts with a Constrained Variational Framework", ICLR (2017).