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Code for "Gradient-based Design of Computational Granular Crystals", Parsa et al.

License: MIT License

Python 100.00%

deep-learning differentiable-simulators gradient-based-optimisation granular-material metamaterial-design metamaterials pytorch unconventional-computing

cgm-torch's Introduction

CGM-Torch: A Differentiable Simulator for Granular Materials

This repository contains the source code for all the experiments in the following paper:

Parsa, A., O'Hern, C. S., Kramer-Bottiglio, R., & Bongard, J. (2024). Gradient-based Design of Computational Granular Crystals. arXiv preprint arXiv:2404.04825.

Installation

Clone this repository and install the following using your preferred python environment or package managment tool:

Usage

Training a new model:

python train.py --name "test" --savedir "./test/" --seed 1

Loading a previously trained model:

python loadModel.py --savedir "./test/" --name "test" --seed 1 --plotName 'AND'

AND Gate

XOR Gate

Citation

If you find our paper or this repository useful or relevant to your work please consider citing us:

@article{parsa2024gradient,
  title={Gradient-based Design of Computational Granular Crystals},
  author={Parsa, Atoosa and O'Hern, Corey S and Kramer-Bottiglio, Rebecca and Bongard, Josh},
  journal={arXiv preprint arXiv:2404.04825},
  year={2024}
}

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