ZhaozhiQIAN / D-CODE-ICLR-2022

D-CODE: Discovering Closed-form ODEs from Observed Trajectories

D-CODE-ICLR-2022

Code for D-CODE: Discovering Closed-form ODEs from Observed Trajectories (ICLR 2022).

Installation

Clone this repository and all submodules (e.g. using git clone --recursive). Python 3.6+ is recommended. Install dependencies as per requirements.txt.

Replicating Experiments

Shell scripts to replicate the experiments can be found in run_all.sh.

To run all the synthetic data experiments:

$ bash run_all.sh

You may also run the experiment steps individually, see run_all.sh. To then produce the figures, run the Jupyter notebooks Result Summary.ipynb, Fig3.ipynb, Fig5.ipynb, rebuttal.ipynb.

Citing

If you use this code, please cite the associated paper:

@inproceedings{NEURIPS2021,
  author = {Qian, Zhaozhi and Kacprzyk, Krzysztof and van der Schaar, Mihaela},
  booktitle = {International Conference on Learning Representations},
  title = {D-CODE: Discovering Closed-form ODEs from Observed Trajectories},
  url = {https://openreview.net/pdf?id=wENMvIsxNN},
  volume = {10},
  year = {2022}
}