Benchmarking Deep Material Networks

In multiscale simulations traditional data driven methods have failed to produce reliable results for highly nonlinear problems in comparison to classical numerical computational homogenization algorithms. Deep material network is a novel hybrid machine learning solver framework which gives reliable predictions in the nonlinear elastic domain with just training using linear elastic responses. The work discusses the implementation of this workflow and successful benchmarking of this technology for state-of-the-art case studies.