Convolution Hierarchical Deep-learning Neural Networks (C-HiDeNN): finite elements, isogeometric analysis, tensor decomposition, and beyond.

MLA

Lu, Ye, et al. “Convolution Hierarchical Deep-Learning Neural Networks (C-HiDeNN): Finite Elements, Isogeometric Analysis, Tensor Decomposition, and Beyond.” Computational Mechanics, vol. 72, no. 2, Aug. 2023, pp. 333–62. EBSCOhost, https://doi.org/10.1007/s00466-023-02336-5.

APA

Lu, Y., Li, H., Zhang, L., Park, C., Mojumder, S., Knapik, S., Sang, Z., Tang, S., Apley, D. W., Wagner, G. J., & Liu, W. K. (2023). Convolution Hierarchical Deep-learning Neural Networks (C-HiDeNN): finite elements, isogeometric analysis, tensor decomposition, and beyond. Computational Mechanics, 72(2), 333–362. https://doi.org/10.1007/s00466-023-02336-5

Chicago

Lu, Ye, Hengyang Li, Lei Zhang, Chanwook Park, Satyajit Mojumder, Stefan Knapik, Zhongsheng Sang, et al. 2023. “Convolution Hierarchical Deep-Learning Neural Networks (C-HiDeNN): Finite Elements, Isogeometric Analysis, Tensor Decomposition, and Beyond.” Computational Mechanics 72 (2): 333–62. doi:10.1007/s00466-023-02336-5.