HyperCAN: Hypernetwork-driven deep parameterized constitutive models for metamaterials

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Introduction

We introduce HyperCAN, a machine learning framework that utilizes hypernetworks to construct adaptable constitutive artificial neural networks for a wide range of beam-based metamaterials under finite deformations. For a detailed explortion of our approach and its applications, please refer to our publication HyperCAN: Hypernetwork-driven deep parameterized constitutive models for metamaterials.

Installation

Dependencies

The framework was developed and tested on Python 3.10.4 using CUDA 12.0. You can install the required dependencies by running: bash pip install -r requirements.txt

Setup

To conduct similar studies as those presented in the publication, start by cloning this repository via

git clone https://github.com/li-zhengz/HyperCAN.git Next, download the data and model checkpoints provided in the ETHZ Research Collection. Unzip the dataset in the dataset folder and the pre-trained model in the model_checkpoint.zip, as shown below. You can also build your own dataset for the given FEM simulationd data by running python loadData.py. . ├── data │ ├── dataset │ │ ├── 6000_train_dataset.pt │ │ ├── 6000_test_truss_dataset.pt │ │ ├── 6000_test_load_dataset.pt │ │ └── 6000_test_load_truss_dataset.pt │ ├── FEM_data (optional) │ │ ├── train │ │ │ └── [...] │ │ ├── test_truss │ │ │ └── [...] │ │ ├── test_load │ │ │ └── [...] │ │ └── test_load_truss │ │ └── [...] ├── model_checkpoint │ ├── icnn_checkpoint.pth │ └── graph_checkpoint.pth

Training

Once you have the tranining data ready, use the following command to start the training process. The training parameters are specified in the config.yaml file. python python main.py

Evaluation

To evaluate the model, run validation.py to obtain the coefficient of determination ($R^2$ score) and the normalized root mean square error (NRMSE) metrics for the trained model across different datasets.

Citation

If this code is useful for your research, please cite our publication.

bibtex @article{ZHENG2024, title = {HyperCAN: Hypernetwork-driven deep parameterized constitutive models for metamaterials}, journal = {Extreme Mechanics Letters}, pages = {102243}, year = {2024}, issn = {2352-4316}, url = {https://www.sciencedirect.com/science/article/pii/S2352431624001238}, author = {Li Zheng and Dennis M. Kochmann and Siddhant Kumar}, }

Author

This code is developed and maintained by Li Zheng.

For further information or inquiries, feel free to contact li.zheng@mavt.ethz.ch.