HomoGenius: a Foundation Model of Homogenization for Rapid Prediction of Effective Mechanical Properties using Neural Operators
<|reference_start|>HomoGenius: a Foundation Model of Homogenization for Rapid Prediction of Effective Mechanical Properties using Neural Operators: Homogenization is an essential tool for studying multiscale physical phenomena. However, traditional numerical homogenization, heavily reliant on finite element analysis, requires extensive computation costs, particularly in handling complex geometries, materials, and high-resolution problems. To address these limitations, we propose a numerical homogenization model based on operator learning: HomoGenius. The proposed model can quickly provide homogenization results for arbitrary geometries, materials, and resolutions, increasing the efficiency by a factor of 80 compared to traditional numerical homogenization methods. We validate effectiveness of our model in predicting the effective elastic modulus on periodic materials (TPMS: Triply Periodic Minimal Surface), including complex geometries, various Poisson's ratios and elastic modulus, and different resolutions for training and testing. The results show that our model possesses high precision, super efficiency, and learning capability.<|reference_end|>
@article{wang2024homogenius:,
    title={HomoGenius: a Foundation Model of Homogenization for Rapid Prediction of
  Effective Mechanical Properties using Neural Operators},
    author={Yizheng Wang, Xiang Li, Ziming Yan, Yuqing Du, Jinshuai Bai, Bokai
  Liu, Timon Rabczuk, Yinghua Liu},
    journal={arXiv preprint arXiv:2404.07943},
    year={2024},
    archivePrefix={arXiv},
    eprint={2404.07943},
    primaryClass={cs.CE cs.LG}
}