Efficient two‐scale simulations of microstructured materials using deep material networks

Abstract

Deep material networks (DMN) are a promising piece of technology for accelerating concurrent multiscale simulations. DMNs are identified by linear elastic pre‐computations on representative volume elements, and serve as high‐fidelity surrogates for full‐field simulations on microstructures with inelastic constituents. The offline training phase is independent of the online evaluation, such that a pre‐trained DMN may be applied for varying material behavior of the constituents. In this contribution, we investigate a two‐scale component simulation of industrial complexity accelerated by DMNs. To this end, a DMN is solved implicitly at every Gauss point to include the microstructure information into the macro simulation.