Augmentation of scarce data -- a new approach for deep-learning modeling of composites

TL;DR

This paper presents a novel data augmentation method using RNNs to enhance deep learning models for micro-mechanical simulations of composites, reducing the need for extensive computational data.

Contribution

The study introduces a new data augmentation approach with RNNs that improves neural network predictions without additional simulations, applicable across various materials and scales.

Findings

Significant improvement in neural network prediction accuracy.

Effective augmentation of scarce data for micro-mechanical modeling.

Potential applicability to diverse materials and length scales.

Abstract

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.