Yiming Zhang1,*, Zhiran Gao1, Xueya Wang1, Qi Liu2
CMES-Computer Modeling in Engineering & Sciences, Vol.134, No.2, pp. 821-833, 2023, DOI:10.32604/cmes.2022.022088
Abstract A large amount of data can partly assure good fitting quality for the trained neural networks. When the quantity
of experimental or on-site monitoring data is commonly insufficient and the quality is difficult to control in
engineering practice, numerical simulations can provide a large amount of controlled high quality data. Once the
neural networks are trained by such data, they can be used for predicting the properties/responses of the engineering
objects instantly, saving the further computing efforts of simulation tools. Correspondingly, a strategy for efficiently
transferring the input and output data used and obtained in numerical simulations to neural networks… More >
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