Abstract:Deep material networks (DMN) are a data‐driven homogenization approach that show great promise for accelerating concurrent two‐scale simulations. As a salient feature, DMNs are solely identified by linear elastic precomputations on representative volume elements. After parameter identification, DMNs act as surrogates for full‐field simulations of such volume elements with inelastic constituents.
In this work, we investigate how the training on linear elastic data, i.e., how the choice of the loss function and … Show more
Set email alert for when this publication receives citations?
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.