June 23, 2020

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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
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.


For model:

For simulations:

  • jax (simple N-body simulations)
  • quijote (Dark matter data)
  • tqdm
  • matplotlib


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.


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:

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