Code for "Discovering Symbolic Models from Deep Learning with Inductive Biases"
Code for "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:
Symbolic regression:
For simulations:
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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