Deep Material Networks Versus Reduced Order Models: A Comparative Study for Multiscale Component Simulations

Deep material networks (DMNs) and reduced order models (ROMs) represent two distinct paradigms for scale bridging in multiscale simulations. ROMs are data‐driven approaches that construct surrogate models for the governing partial differential equations of the microscale cell problem. In contrast, DMNs rely on physics‐based building blocks identified from data generated through linear elastic precomputations. Both approaches serve as efficient surrogates for full‐field microscale simulations. In this work, we present a systematic comparison of DMNs and ROMs at two levels: first, at the representative volume element (RVE) level, and second, at the system level within a multiscale component simulation of a lye container back‐wall within an industrial washing machine subjected to high‐cycle fatigue.