Graph Network Simulator (GNS)

Krishna Kumar, The University of Texas at Austin.

Joseph Vantassel, Texas Advanced Computing Center, UT Austin.

CS224W Project

We use this repo as the starting point to simulate a cloth drop on a ball. With our enhancement, we are able to generating below result:

To see how we enhance the original model, please refer to this link for details: https://medium.com/@bill.s.lin1/graph-network-based-simulator-of-cloth-falling-through-obstacles-cef3e066a41e

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Graph Network-based Simulator (GNS) is a framework for developing generalizable, efficient, and accurate machine learning (ML)-based surrogate models for particulate and fluid systems using Graph Neural Networks (GNNs). GNS code is a viable surrogate for numerical methods such as Material Point Method, Smooth Particle Hydrodynamics and Computational Fluid dynamics. GNS exploits distributed data parallelism to achieve fast multi-GPU training. The GNS code can handle complex boundary conditions and multi-material interactions.

Run GNS

Training

python3 -m gns.train --data_path="<input-training-data-path>" --model_path="<path-to-load-save-model-file>" --output_path="<path-to-save-output>" -ntraining_steps=100

Our cloth setting

python gns/train.py --data_path data/cloth/ --output_path data/cloth/output/ --model_path data/cloth/model/

Resume training

To resume training specify model_file and train_state_file:

python3 -m gns.train --data_path="<input-training-data-path>" --model_path="<path-to-load-save-model-file>" --output_path="<path-to-save-output>"  --model_file="model.pt" --train_state_file="train_state.pt" -ntraining_steps=100

Our cloth setting

python gns/train.py --data_path data/cloth/ --output_path data/cloth/output/ --model_path data/cloth/model/ --model_file model-30000.pt --train_state_file train_state-30000.pt

Rollout

python3 -m gns.train --mode="rollout" --data_path="<input-data-path>" --model_path="<path-to-load-save-model-file>" --output_path="<path-to-save-output>" --model_file="model.pt" --train_state_file="train_state.pt"

Our cloth setting

python gns/train.py --mode rollout --data_path data/cloth/ --output_path data/cloth/output/ --model_path data/cloth/model/ --train_state_file train_state-30000.pt --model_file model-30000.pt

Render

 python3 -m gns.render_rollout --rollout_path="<path-containing-rollout-file>/rollout_0.pkl" 

Our cloth setting

python gns/render_rollout.py --rollout_path data/cloth/output/

The renderer also writes .vtu files to visualize in ParaView.

Datasets

Original paper dataset

The data loader provided with this PyTorch implementation utilizes the more general .npz format. The .npz format includes a list of tuples of arbitrary length where each tuple is for a different training trajectory and is of the form (position, particle_type). position is a 3-D tensor of shape (n_time_steps, n_particles, n_dimensions) and particle_type is a 1-D tensor of shape (n_particles).

The dataset contains:

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

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

We provide the following datasets:

• WaterDropSample (smallest dataset)
• Sand
• SandRamps

Download the dataset from DesignSafe DataDepot. If you are using this dataset please cite Vantassel and Kumar., 2022

Our cloth setting

We used the open source Taichi physics simulator to generate the source-of-truth dataset by running 1000 experiments of a piece of cloth falling onto a spherical obstacle. You can find the dataset that we generated here.

Building environment on TACC LS6 and Frontera

• to setup a virtualenv

sh ./build_venv.sh

• check tests run sucessfully.
• start your environment

source start_venv.sh 

Inspiration

PyTorch version of Graph Network Simulator based on https://arxiv.org/abs/2002.09405 and https://github.com/deepmind/deepmind-research/tree/master/learning_to_simulate.

Acknowledgement

This code is based upon work supported by the National Science Foundation under Grant OAC-2103937.

Citation

Repo

Kumar, K., & Vantassel, J. (2022). Graph Network Simulator: v1.0.1 (Version v1.0.1) [Computer software]. https://doi.org/10.5281/zenodo.6658322

Dataset

Vantassel, Joseph; Kumar, Krishna (2022) “Graph Network Simulator Datasets.” DesignSafe-CI. https://doi.org/10.17603/ds2-0phb-dg64 v1