gnn-for-particle-crushing's Introduction

GNN-For-Particle-Crushing

Code for the paper Graph Neural Networks-based Hybrid Framework For Predicting Particle Crushing Strength.

We have generated 45,000 numerical simulations for particle crushing with 900 different particle types in total, the Cartesian product of 20 different particle diameters, 15 different scale shapes in the (X, Y, Z) axes, and 3 different compression axes under one-dimensional compression. Download data_files from Google Drive and place the directory in the current folder.

Setup the Environment

conda create -n particle python=3.9 -y
conda activate particle
pip install -r requirements.txt. My torch version is torch-1.10.2+cu113.
Install the dependency of torch_geoemtric according to PyG. My PYG version is torch-1.10.2+cu113, and I recommend to download these dependency packages from PyG-torch.10.2+cu113.
pip install torch-geometric==2.0.3
bash ./init.sh to create useful directories.

Run non-Deep methods

python main_baselines.py --model Linear
python main_baselines.py --model Ridge
python main_baselines.py --model RF
python main_baselines.py --model XGB
python main_baselines.py --model LGB

Run DNNs

python main_nn.py --model MLP
python main_nn.py --model MeshNet
python main_nn.py --model GIN
python main_nn.py --model ExpC

Other parameters

Set the task, --test-choice diameter | scale | rotation, refers to the diameter, shape, and axis task in our paper.

Cite us

@article{zheng2023graph,
  title={Graph Neural Networks-based Hybrid Framework For Predicting Particle Crushing Strength},
  author={Zheng, Tongya and Zhang, Tianli and Guan, Qingzheng and Huang, Wenjie and Feng, Zunlei and Song, Mingli and Chen, Chun},
  journal={arXiv:2307.13909 },
  year={2023},
  pages={1--43},
  publisher={arxiv}
}

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