  High-fidelity full-field micro-mechanical modeling of the non-linear
path-dependent materials demands a substantial computational effort. Recent
trends in the field incorporates data-driven Artificial Neural Networks (ANNs)
as surrogate models. However, ANNs are inherently data-hungry, functioning as a
bottleneck for the development of high-fidelity data-driven models. This study
introduces a novel approach for data augmentation, expanding an original
dataset without additional computational simulations. A Recurrent Neural
Network (RNN) was trained and validated on high-fidelity micro-mechanical
simulations of elasto-plastic short fiber reinforced composites. The obtained
results showed a considerable improvement of the network predictions trained on
expanded datasets using the proposed data augmentation approach. The proposed
method for augmentation of scarce data may be used not only for other kind of
composites, but also for other materials and at different length scales, and
hence, opening avenues for innovative data-driven models in materials science
and computational mechanics.
