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:
- pytorch
- pytorch-geometric
- numpy
- Eureqa (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: