learning_to_simulate

Learning to Simulate Complex Physics with Graph Networks (ICML 2020)

ICML poster: icml.cc/virtual/2020/poster/6849

Video site: sites.google.com/view/learning-to-simulate

ArXiv: arxiv.org/abs/2002.09405

If you use the code here please cite this paper:

@inproceedings{sanchezgonzalez2020learning,
  title={Learning to Simulate Complex Physics with Graph Networks},
  author={Alvaro Sanchez-Gonzalez and
          Jonathan Godwin and
          Tobias Pfaff and
          Rex Ying and
          Jure Leskovec and
          Peter W. Battaglia},
  booktitle={International Conference on Machine Learning},
  year={2020}
}

Example usage: train a model and display a trajectory

After downloading the repo, and from the parent directory. Install dependencies:

pip install -r learning_to_simulate/requirements.txt
mkdir -p /tmp/rollous

Download dataset (e.g. WaterRamps):

mkdir -p /tmp/datasets
bash ./learning_to_simulate/download_dataset.sh WaterRamps /tmp/datasets

Train a model:

mkdir -p /tmp/models
python -m learning_to_simulate.train \
    --data_path=/tmp/datasets/WaterRamps \
    --model_path=/tmp/models/WaterRamps

Generate some trajectory rollouts on the test set:

mkdir -p /tmp/rollouts
python -m learning_to_simulate.train \
    --mode="eval_rollout" \
    --data_path=/tmp/datasets/WaterRamps \
    --model_path=/tmp/models/WaterRamps \
    --output_path=/tmp/rollouts/WaterRamps

Plot a trajectory:

python -m learning_to_simulate.render_rollout \
    --rollout_path=/tmp/rollouts/WaterRamps/rollout_test_0.pkl

Datasets

Datasets are available to download via:

• Metadata file with dataset information (sequence length, dimensionality, box bounds, default connectivity radius, statistics for normalization, ...):

  https://storage.googleapis.com/learning-to-simulate-complex-physics/Datasets/{DATASET_NAME}/metadata.json

• TFRecords containing data for all trajectories (particle types, positions, global context, ...):

  https://storage.googleapis.com/learning-to-simulate-complex-physics/Datasets/{DATASET_NAME}/{DATASET_SPLIT}.tfrecord

Where:

• {DATASET_SPLIT} is one of:

  • train
  • valid
  • test

• {DATASET_NAME} one of the datasets following the naming used in the paper:

  • WaterDrop
  • Water
  • Sand
  • Goop
  • MultiMaterial
  • RandomFloor
  • WaterRamps
  • SandRamps
  • FluidShake
  • FluidShakeBox
  • Continuous
  • WaterDrop-XL
  • Water-3D
  • Sand-3D
  • Goop-3D

The provided script ./download_dataset.sh may be used to download all files from each dataset into a folder given its name.

An additional smaller dataset WaterDropSample, which includes only the first two trajectories of WaterDrop for each split, is provided for debugging purposes.

Code structure

• train.py: Script for training, evaluating and generating rollout trajectories.
• learned_simulator.py: Implementation of the learnable one-step model that returns the next position of the particles given inputs. It includes data preprocessing, Euler integration, and a helper method for building normalized training outputs and targets.
• graph_network.py: Implementation of the graph network used at the core of the learnable part of the model.
• render_rollout.py: Visualization code for displaying rollouts such as the example animation.
• {noise/connectivity/reading}_utils.py: Util modules for adding noise to the inputs, computing graph connectivity and reading datasets form TFRecords.
• model_demo.py: example connecting the model to input dummy data.

Note this is a reference implementation not designed to scale up to TPUs (unlike the one used for the paper). We have tested that the model can be trained with a batch size of 2 on a single NVIDIA V100 to reach similar qualitative performance (except for the XL and 3D datasets due to OOM).