# MilesCranmer/symbolic_deep_learning

Code for "Discovering Symbolic Models from Deep Learning with Inductive Biases"

repo name | MilesCranmer/symbolic_deep_learning |

repo link | https://github.com/MilesCranmer/symbolic_deep_learning |

homepage | |

language | Jupyter Notebook |

size (curr.) | 6483 kB |

stars (curr.) | 154 |

created | 2020-06-16 |

license | MIT License |

# Discovering Symbolic Models from Deep Learning with Inductive Biases

This repository is the official implementation of Discovering Symbolic Models from Deep Learning with Inductive Biases.

Miles Cranmer, Alvaro Sanchez-Gonzalez, Peter Battaglia, Rui Xu, Kyle Cranmer, David Spergel, Shirley Ho

Check out our Blog, Paper, Video, and Interactive Demo.

## Requirements

For model:

- pytorch
- pytorch-geometric
- numpy
- Eureqa (symbolic regression)

For simulations:

## Training

To train an example model from the paper, try out the demo.

Full model definitions are given in `models.py`

. Data is generated from `simulate.py`

.

## Results

We train on simulations produced by the following equations: giving us time series:

We recorded performance for each model: and also measured how well each model’s messages correlated with a linear combination of forces:

Finally, we trained on a dark matter simulation and extracted the following equations from the message function: