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Learning Mesh-Based Simulation with Graph Networks (ICLR 2021)

Video site: sites.google.com/view/meshgraphnets

Paper: arxiv.org/abs/2010.03409

If you use the code here please cite this paper:

@inproceedings{pfaff2021learning,
  title={Learning Mesh-Based Simulation with Graph Networks},
  author={Tobias Pfaff and
          Meire Fortunato and
          Alvaro Sanchez-Gonzalez and
          Peter W. Battaglia},
  booktitle={International Conference on Learning Representations},
  year={2021}
}

Setup

Prepare environment, install dependencies:

virtualenv --python=python3.6 "${ENV}"
${ENV}/bin/activate
pip install -r meshgraphnets/requirements.txt

Download a dataset:

mkdir -p ${DATA}
bash meshgraphnets/download_dataset.sh flag_simple ${DATA}

Running the model

Train a model:

python -m meshgraphnets.run_model --mode=train --model=cloth \
    --checkpoint_dir=${DATA}/chk --dataset_dir=${DATA}/flag_simple

Generate some trajectory rollouts:

python -m meshgraphnets.run_model --mode=eval --model=cloth \
    --checkpoint_dir=${DATA}/chk --dataset_dir=${DATA}/flag_simple \
    --rollout_path=${DATA}/rollout_flag.pkl

Plot a trajectory:

python -m meshgraphnets.plot_cloth --rollout_path=${DATA}/rollout_flag.pkl

Datasets

Datasets can be downloaded using the script download_dataset.sh. They contain a metadata file describing the available fields and their shape, and tfrecord datasets for train, valid and test splits. Dataset names match the naming in the paper. The following datasets are available:

airfoil
cylinder_flow
deforming_plate
flag_minimal
flag_simple
flag_dynamic
sphere_simple
sphere_dynamic

flag_minimal is a truncated version of flag_simple, and is only used for integration tests.