https://github.com/MilesCranmer/symbolic_deep_learning

Code for "Discovering Symbolic Models from Deep Learning with Inductive Biases"
https://github.com/MilesCranmer/symbolic_deep_learning

Last synced: 4 months ago
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Code for "Discovering Symbolic Models from Deep Learning with Inductive Biases"

• Host: GitHub
• URL: https://github.com/MilesCranmer/symbolic_deep_learning
• Owner: MilesCranmer
• License: mit
• Created: 2020-06-16T22:43:01.000Z (over 4 years ago)
• Default Branch: master
• Last Pushed: 2023-11-20T01:58:13.000Z (over 1 year ago)
• Last Synced: 2024-10-22T12:54:53.171Z (4 months ago)
• Language: Python
• Size: 6.19 MB
• Stars: 722
• Watchers: 28
• Forks: 132
• Open Issues: 4
• Metadata Files:
  • Readme: README.md
  • License: LICENSE

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README

        
# [Discovering Symbolic Models from Deep Learning with Inductive Biases](https://arxiv.org/abs/2006.11287)

This [repository](https://github.com/MilesCranmer/symbolic_deep_learning) is the official implementation of [Discovering Symbolic Models from Deep Learning with Inductive Biases](https://arxiv.org/abs/2006.11287). 

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

Check out our [Blog](https://astroautomata.com/paper/symbolic-neural-nets/), [Paper](https://arxiv.org/abs/2006.11287), [Video](https://youtu.be/2vwwu59RPL8), and [Interactive Demo](https://colab.research.google.com/github/MilesCranmer/symbolic_deep_learning/blob/master/GN_Demo_Colab.ipynb).

[![](images/discovering_symbolic_eqn_gn.png)](https://astroautomata.com/paper/symbolic-neural-nets/)

## Requirements

For model:

- pytorch

- [pytorch-geometric](https://github.com/rusty1s/pytorch_geometric)

- numpy

Symbolic regression:

- [PySR](https://github.com/MilesCranmer/PySR), our new open-source Eureqa alternative

For simulations:

- [jax](https://github.com/google/jax) (simple N-body simulations)

- [quijote](https://github.com/franciscovillaescusa/Quijote-simulations) (Dark matter data; optional)

- tqdm

- matplotlib

## Training

To train an example model from the paper, try out the [demo](https://colab.research.google.com/github/MilesCranmer/symbolic_deep_learning/blob/master/GN_Demo_Colab.ipynb).

Full model definitions are given in `models.py`. Data is generated from `simulate.py`.

## Results

We train on simulations produced by the following equations:

![](images/simulation_equations.png)

giving us time series:

![](images/simulations.png)

We recorded performance for each model:

![](images/test_prediction.png)

and also measured how well each model's messages

correlated with a linear combination of forces:

![](images/equal_to_forces.png)

Finally, we trained on a dark matter simulation and extracted the following equations

from the message function:

![](images/dark_matter.png)