Abstract
Data detailed on Machine Learning have been presented. According to news reporting originating from Beijing, People’s Republic of China, by NewsRx correspondents, research stated, “In this study, a novel framework was presented for accelerating the prediction of the mechanical response of honeycomb structures under dynamic crushing, using 2D cells to surrogate 3D honeycomb structures by machine learning (ML). The sizes of different honeycomb structures were designed and the necessary training data obtained through finite element (FE) simulations, but without using any explicit design parameters of the honeycomb cells in the ML model.” Financial supporters for this research include National Natural Science Foundation of China (NSFC), Science and Technology innovation Program of Beijing institute of technology, State Key Laboratory of Explosion Science and Technology (Beijing Institute of Technology), Academic Start-up Program for Young Teachers (Beijing Institute of Technology), Sci-ence and Technology Innovation Program of Beijing institute of tech-nology.
NETL
NSTL
Elsevier