Recent trends confirm an increasing demand for Short Fibre Reinforced Composites (SFRCs), partially because they are suitable for injection moulding fabrication processes and possess a high strength-to-density ratio compared to unfilled polymer matrices, making them applicable in lightweight applications. To develop physics-based models and establish structure-property relationships for SFRCs, micro-mechanical models are used. However, traditional high-fidelity micro-mechanical methods are computationally expensive, especially for non-linear path-dependent modelling [Schneider et al., (2016)]. Given the significance of numerical modelling in iterative design processes, there is a current need for more efficient approaches for computational modelling of elasto-plastic behaviour of SFRCs. Recently, data-driven Artificial Neural Networks (ANNs) have been used as surrogate models, since they enable parallel computing, and conduct fast and efficient calculations. However, the data-hungry nature of ANNs remains a challenge, since creating large datasets of high-fidelity simulations requires extensive computational efforts and time. As a possible solution, transfer learning has been used recently, where multiple datasets are used to train an ANN [Cheung and Mirkhalaf (2024)]. While this technique produces accurate models, it is limited to scenarios containing multiple datasets. To address this challenge, we have developed a novel data augmentation approach by introducing random rotations. This method effectively expands a dataset of high-fidelity simulations of non-linear elasto plastic response of SFRCs without a need for additional simulations. Recurrent Neural Networks (RNNs) trained on the augmented datasets exhibit significant improvement in predicting high-fidelity micro-mechanical simulations. Our data augmentation method may also be used for modelling other materials, and hence, offers a promising solution for data scarcity issue. This opens possibilities for innovative advancements in deep learning modelling in materials science.