Accelerating Analysis for Structure Design via Deep Learning Surrogate Models

Using computer simulation tools such as finite element analysis (FEA) to perform material stress is a common design method in engineering practice. In order model more realistic real-world systems, models have become complex, and calculation becomes expensive result. The rise of artificial intelligence technologies has made it possible integrate deep learning methods analysis. Herein, FEA software employed obtain large number cases training samples uses fully connected neural network long-short-term memory surrogate models, which can predict the distribution sequence process bullet impacting target plates with different materials. These give results similar 92.19% 92.41% accuracy, respectively. experimental show that great potential