# nn_physical_concepts

**Repository Path**: booq_Lab/nn_physical_concepts

## Basic Information

- **Project Name**: nn_physical_concepts
- **Description**: No description available
- **Primary Language**: Unknown
- **License**: Apache-2.0
- **Default Branch**: master
- **Homepage**: None
- **GVP Project**: No

## Statistics

- **Stars**: 0
- **Forks**: 0
- **Created**: 2021-09-23
- **Last Updated**: 2021-09-23

## Categories & Tags

**Categories**: Uncategorized

**Tags**: None

## README

# 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](https://arxiv.org/abs/1807.10300) (2018).

This repository contains the trained [Tensorflow](https://www.tensorflow.org) 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](https://openreview.net/forum?id=Sy2fzU9gl) [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](https://stackoverflow.com/questions/10738919/how-do-i-add-a-path-to-pythonpath-in-virtualenv) for instructions for doing this in a virtual environment. Without a virtual environment, see [here](https://stackoverflow.com/questions/3402168/permanently-add-a-directory-to-pythonpath).
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).