Machine Learning for Composites

Deep Learning for Stress Prediction in Heterogeneous Media

Stress analysis of heterogeneous media, like architected and composite materials, using finite element method (FEM) has become commonplace in design and analysis. However, determining stress distributions in heterogeneous media using FEM can be computationally expensive in situations like optimization and multi-scaling, where several design iterations are required to be tested iteratively until convergence.

To address this, we utilize Deep Learning for developing a set of novel Difference-based Neural Network (DiNN) frameworks based on engineering and statistics knowledge to determine stress distribution in heterogeneous media, for the first time, with special focus on discontinuous domains that manifest high stress concentrations.