Convolutional Hierarchical Deep Learning Neural Networks-Tensor Decomposition (C-HiDeNN-TD): a scalable surrogate modeling approach for large-scale physical systems
<|reference_start|>Convolutional Hierarchical Deep Learning Neural Networks-Tensor Decomposition (C-HiDeNN-TD): a scalable surrogate modeling approach for large-scale physical systems: A common trend in simulation-driven engineering applications is the ever-increasing size and complexity of the problem, where classical numerical methods typically suffer from significant computational time and huge memory cost. Methods based on artificial intelligence have been extensively investigated to accelerate partial differential equations (PDE) solvers using data-driven surrogates. However, most data-driven surrogates require an extremely large amount of training data. In this paper, we propose the Convolutional Hierarchical Deep Learning Neural Network-Tensor Decomposition (C-HiDeNN-TD) method, which can directly obtain surrogate models by solving large-scale space-time PDE without generating any offline training data. We compare the performance of the proposed method against classical numerical methods for extremely large-scale systems.<|reference_end|>
@article{guo2024convolutional,
    title={Convolutional Hierarchical Deep Learning Neural Networks-Tensor
  Decomposition (C-HiDeNN-TD): a scalable surrogate modeling approach for
  large-scale physical systems},
    author={Jiachen Guo, Chanwook Park, Xiaoyu Xie, Zhongsheng Sang, Gregory J.
  Wagner, Wing Kam Liu},
    journal={arXiv preprint arXiv:2409.00329},
    year={2024},
    archivePrefix={arXiv},
    eprint={2409.00329},
    primaryClass={cs.CE}
}