Ye Lu1, Hengyang Li1, Sourav Saha2, Satyajit Mojumder2, Abdullah Al Amin1, Derick Suarez1, Yingjian Liu3, Dong Qian3, Wing Kam Liu1,*
CMES-Computer Modeling in Engineering & Sciences, Vol.129, No.3, pp. 1351-1371, 2021, DOI:10.32604/cmes.2021.017719
Abstract This paper presents the concept of reduced order machine learning finite element (FE) method. In particular, we propose an example of such method, the proper generalized decomposition (PGD) reduced hierarchical deeplearning neural networks (HiDeNN), called HiDeNN-PGD. We described first the HiDeNN interface seamlessly with the current commercial and open source FE codes. The proposed reduced order method can reduce significantly the degrees of freedom for machine learning and physics based modeling and is able to deal with high dimensional problems. This method is found more accurate than conventional finite element methods with a small portion of degrees of freedom. Different… More >
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