  The demand for fast and accurate structural analysis is becoming increasingly
more prevalent with the advance of generative design and topology optimization
technologies. As one step toward accelerating structural analysis, this work
explores a deep learning based approach for predicting the stress fields in 2D
linear elastic cantilevered structures subjected to external static loads at
its free end using convolutional neural networks (CNN). Two different
architectures are implemented that take as input the structure geometry,
external loads, and displacement boundary conditions, and output the predicted
von Mises stress field. The first is a single input channel network called
SCSNet as the baseline architecture, and the second is the multi-channel input
network called StressNet. Accuracy analysis shows that StressNet results in
significantly lower prediction errors than SCSNet on three loss functions, with
a mean relative error of 2.04% for testing. These results suggest that deep
learning models may offer a promising alternative to classical methods in
structural design and topology optimization. Code and dataset are available at
https://github.com/zhenguonie/stress_net

Machine Learning;Machine Learning