LG-ODE is an overall framework for learning continuous multi-agent system dynamics from irregularly-sampled partial observations considering graph structure.
You can see our Neurips 2020 paper “Learning Continuous System Dynamics from Irregularly-Sampled Partial Observations” for more details.
This implementation of LG-ODE is based on Pytorch Geometric API.
Generate simulated datasets (spring, charged particles) by running:
cd data
python generate_dataset.py This generates the springs dataset, use --simulation charged for charged particles.
As simulated data is too large, we provide a toy-data from spring dataset and can be found under data/example_data
Motion dataset can be downloaded CMU MoCap
This implementation is based on pytorch_geometric. To run the code, you need the following dependencies:
-
- torch-cluster==1.5.3
- torch-scatter==2.0.4
- torch-sparse==0.6.1
Execute the following scripts to train on the sampled data from spring system:
python run_models.pyThere are some key options of this scrips:
-
--sample-percent-train: This is the observed percentage in your training data. -
--sample-percent-test: This is the observed percentage in your testing data. -
--solver: This is for choosing your ODE Solver. -
--extrap: Set True to run in the extrapolation mode, otherwise run in the interpolation mode.
The details of other optional hyperparameters can be found in run_models.py.
Please consider citing the following paper when using our code for your application.
@inproceedings{LG-ODE,
title={Learning Continuous System Dynamics from Irregularly-Sampled Partial Observations},
author={Zijie Huang and Yizhou Sun and Wei Wang},
booktitle={Advances in Neural Information Processing Systems},
year={2020}
}