Teaching solid mechanics to artificial intelligence-a fast solver for heterogeneous materials

We propose a deep neural network (DNN) as a fast surrogate model for local stress calculations in inhomogeneous non-linear materials. We show that the DNN predicts the local stresses with 3.8% mean absolute percentage error (MAPE) for the case of heterogeneous elastic media and a mechanical contrast of up to factor of 1.5 among neighboring domains, while performing 103 times faster than spectral solvers. The DNN model proves suited for reproducing the stress distribution in geometries different from those used for training. In the case of elasto-plastic materials with up to 4 times mechanical contrast in yield stress among adjacent regions, the trained model simulates the micromechanics with a MAPE of 6.4% in one single forward evaluation of the network, without any iteration. The results reveal an efficient approach to solve non-linear mechanical problems, with an acceleration up to a factor of 8300 for elastic-plastic materials compared to typical solvers.

Automated summary

A deep neural network (DNN) is proposed as a fast surrogate model for local stress calculations in inhomogeneous non-linear materials, showing efficient performance and high accuracy in predicting stress distribution. The DNN model is capable of simulating micromechanics in elastic-plastic materials with significant acceleration compared to typical solvers.