Skip to content

Latest commit

 

History

History
120 lines (99 loc) · 5.76 KB

README.md

File metadata and controls

120 lines (99 loc) · 5.76 KB
Raw

Meshed-based fluid simulation with GNNs

Re-implementation of Learning Mesh-Based Simulation with Graph Networks for cylinder_flow in PyTorch based on this blog post.

Look at results.md for a summary!

Installation

Get the code

git clone [email protected]:BurgerAndreas/gnn-physics.git
# git clone https://github.com/BurgerAndreas/gnn-physics.git

Setup Conda environment (Tested on Ubuntu 22.04, RTX 3060, Cuda 12.3)

conda create -n meshgnn python=3.11 -y
conda activate meshgnn
pip3 install -r requirements.txt

# or try by hand
conda install -c conda-forge nbformat jupyter plotly matplotlib mediapy pip tqdm gdown -y
pip3 install torch torchvision torchaudio torch_scatter torch_sparse torch_cluster torch_spline_conv torch-geometric torchdata 
pip3 install black hydra-core
pip3 install tensorflow tensorrt protobuf==3.20.3

Run

Download the small dataset (1GB) from google drive

cd data/datasets/cylinder_flow_pyg/
# https://drive.google.com/file/d/1AmQwNt2zsLnUSUWcH_f8rGIPY9VhPQZt/view?usp=sharing
wget --load-cookies /tmp/cookies.txt "https://docs.google.com/uc?export=download&confirm=$(wget --quiet --save-cookies /tmp/cookies.txt --keep-session-cookies --no-check-certificate 'https://docs.google.com/uc?export=download&id=1AmQwNt2zsLnUSUWcH_f8rGIPY9VhPQZt' -O- | sed -rn 's/.*confirm=([0-9A-Za-z_]+).*/\1\n/p')&id=1AmQwNt2zsLnUSUWcH_f8rGIPY9VhPQZt" -O data_pt.tgz && rm -rf /tmp/cookies.txt

# Unzip the data
tar -zxvf data_pt.tgz

Run the code

cd ../../..
# Run with default settings
python run_gnn.py
# Try additional configs like this:
python run_gnn.py +noise=paper +datasize=small
# Plot training loss & predictions from loaded checkpoints like this:
python plot.py +datasize=medium
python animate.py +datasize=medium

Dataset

The original cylinder_flow dataset contains 1,200 trajectories with 600 timesteps each. The data is in the .tfrecord format. .tfrecord is highly optimized, but only works with Tensorflow and can be hard to handle.

I simplified the dataset to 4 trajectories (3 train, 1 test) saved as numpy arrays in a .hdf5 file. The 4 trajectories are provided via the google drive link avove.

Optional: get more data

If you want to download the full original .tfrecord dataset for cylinder_flow (16 GB)

chmod +x ./data/datasets/download_dataset.sh
bash ./data/datasets/download_dataset.sh cylinder_flow ./data/datasets

If you want to convert the .tfrecord dataset to numpy in .hdf5

conda activate meshgnn
# -num_traj -1 means convert all trajectories
python ./data/datasets/tfrecord_to_hdf5.py -in 'data/datasets/cylinder_flow/train' -out 'data/datasets/cylinder_flow_hdf5/train' --num_traj 3 
python ./data/datasets/tfrecord_to_hdf5.py -in 'data/datasets/cylinder_flow/test' -out 'data/datasets/cylinder_flow_hdf5/test' --num_traj 1

If you want to convert the .hdf5 dataset to PyTorch graphs .pt

conda activate meshgnn
python ./data/datasets/hdf5_to_pyg.py -in 'data/datasets/cylinder_flow_hdf5/train.hdf5' -out 'data/datasets/cylinder_flow_pyg/train.pt'
python ./data/datasets/hdf5_to_pyg.py -in 'data/datasets/cylinder_flow_hdf5/test.hdf5' -out 'data/datasets/cylinder_flow_pyg/test.pt'

Optional: get prior blog data

I basde my code on this blog post which ships with some data in .pt format. Sadly they did not include the code they used to transform the data. My code still works on their data. In practice their data performs worse than my data conversion, for unknown reasons.

If you want to download their data:

python ./data/datasets/download_pyg_stanford_data.py

Future Work

The original codebase of the paper implements the cylinder_flow and flag_simple domains.

  • Change ./data/datasets/hdf5_to_pyg.py to work with all datasets with different features

The original codebase also does not contain the prediction of the sizing field and the corresponding remesher. Out of time constraints we do not implement the sizing field prediction + remesher either. To implement sizing field prediction:

  • Build prediction head and combine with existing GNN
  • Build sizing-based remesher (pseudo-code can be found in this paper and A3 of the original paper)
  • Adapt training loop to learn sizing field prediction on flag_dynamic_sizing (Only the flag_dynamic_sizing (36 GB) and sphere_dynamic_sizing datasets include the necessary data to learn the sizing field)

Ressources

Follow-up papers