PARC: Physics-Aware Recurrent Convolutions

Official implementation of Nguyen, P.C.H., Nguyen, Y.T., Choi, J.B., Seshadri, P., Udaykumar, H.S., & Baek, S.S. (2023). PARC: Physics-Aware Recurrent Convolutional Neural Networks to Assimilate Meso-scale Reactive Mechanics of Energetic Materials. Science Advances, 9(17):eadd6868.

Getting Started

Virtual Environment

It is recommended to create a virtual environment using Anaconda or Miniconda and install PARC within the virtual environment. To create a virtual environment, type the following command in your terminal or command prompt.

conda create -n parc python=3.8 ipykernel

Once the virtual environment has been created, run the following to activate the environment.

conda activate parc

Dependencies

TensorFlow

Tested and developed on TensorFlow 2.8.0. It should be compatible with other TensorFlow2 versions, but we haven't tested. Make sure you follow the installation instructions in https://www.tensorflow.org/install to install TensorFlow2 according to your system configuration.

pip install tensorflow

Other Dependencies

pip install -r requirements.txt

Clone This Repository

git clone https://github.com/stephenbaek/parc.git

cd parc

Run Examples

The details for using the PARC model is best described in the demos/PARC_demo.ipynb.

Citation

To cite this work, please use the following:

@article{
  nguyen2023parc,
  author = {Phong C. H. Nguyen  and Yen-Thi Nguyen  and Joseph B. Choi
   and Pradeep K. Seshadri  and H. S. Udaykumar  and Stephen S. Baek },
  title = {{PARC}: Physics-aware recurrent convolutional neural networks to
   assimilate meso scale reactive mechanics of energetic materials},
  journal = {Science Advances},
  volume = {9},
  number = {17},
  pages = {eadd6868},
  year = {2023},
  doi = {10.1126/sciadv.add6868},
  URL = {https://www.science.org/doi/abs/10.1126/sciadv.add6868}
}

@article{
  nguyen2023parcel,
  author = {Phong C. H. Nguyen and Yen-Thi Nguyen and Pradeep K. Seshadri
   and Joseph B. Choi and H. S. Udaykumar and Stephen Baek},
  title = {A Physics-Aware Deep Learning Model for Energy Localization in
   Multiscale Shock-To-Detonation Simulations of Heterogeneous Energetic Materials},
  journal = {Propellants, Explosives, Pyrotechnics},
  volume = {48},
  number = {4},
  pages = {e202200268},
  year = {2023},
  doi = {https://doi.org/10.1002/prep.202200268},
  url = {https://onlinelibrary.wiley.com/doi/abs/10.1002/prep.202200268}
}