A mechanics-informed machine learning approach for modeling the elastoplastic behavior of fiber-reinforced composites

When machine learning (ML) techniques are used to predict the elastoplastic behavior of a fiber-reinforced composite, a large training database is typically required due to the complicated network architecture that is built to characterize the anisotropic plasticity. In this paper, a mechanics-informed ML approach that enables to employ a small training database is proposed to predict the elastoplastic behaviors of a unidirectional fiber-reinforced composite by incorporating mechanics-based decompositions of strain and stress into an artificial neural network (ANN). The built ANNs have simple structures and greatly enhance the prediction capability of the ML-based constitutive model when using a small database. The ML approach is further improved to predict the effect of the loading path. Direct numerical simulations (DNSs) based on a representative volume element are carried out to generate the datasets used for training and validating the ML-based constitutive model. By comparing the results obtained when using DNS and ML, it is shown that the proposed ML-based constitutive model offers excellent predictive accuracy even when using a small training database, and it provides much better results than a ML approach that does not include the decomposition of strain and stress.