Material database construction for data-driven computing via a continuous path-following method

Data-driven computational homogenization has been proposed recently for the analyses of composite struc-tures. Its basic idea is to construct an equivalent stress-strain database of composites via offline homogenization on the representative volume element and conduct online macroscopic simulation through distance-minimizing data-driven computing. Thanks to the scale separation of concurrent multiscale systems, this framework allows for improving online computational efficiency. However, high-density database construction in the offline stage remains a burdensome and time-consuming task. To this end, this work proposed an efficient approach that associates computational homogenization with the Asymptotic Numerical Method (ANM) to construct a high-density database. Being a reliable and efficient perturbation technique, the ANM allows for accurate tracking of the displacement-load paths and easily generates abundant equivalent stress-strain data on the paths. A fiber reinforced composite material with fiber buckling has been considered to demonstrate the accuracy and efficiency of the proposed method for the database construction of composites.