Test-time data augmentation: improving predictions of recurrent neural network models of composites

TL;DR

This paper introduces a novel test-time data augmentation method for RNNs predicting path-dependent material behavior, significantly improving accuracy, shape consistency, and uncertainty estimation in composite material modeling.

Contribution

The study pioneers the application of test-time data augmentation to RNNs, enhancing prediction accuracy and providing uncertainty estimates in modeling composite materials.

Findings

TTA improves RNN prediction accuracy for composite materials.

Back rotated predictions increase shape consistency.

Uncertainty estimates correlate with prediction errors.

Abstract

Recurrent Neural Networks (RNNs) have emerged as an interesting alternative to conventional material modeling approaches, particularly for nonlinear path dependent materials. Remarkable computational enhancements are obtained using RNNs compared to classical approaches such as the computational homogenization method. However, RNN predictive errors accumulate, leading to issues when predicting temporal dependencies in time series data. This study aims to address and mitigate inaccuracies induced by neural networks in predicting path dependent plastic deformations of short fiber reinforced composite materials. We propose using an approach of Test Time data Augmentation (TTA), which, to the best of the authors knowledge, is previously untested in the context of RNNs. The method is based on augmenting the input test data using random rotations and subsequently rotating back the predicted output signal. By aggregating the back rotated predictions, a more accurate prediction compared to individual predictions is obtained. Our analysis also demonstrates improved shape consistency between the prediction and the target pseudo time signal. Additionally, this method provides an uncertainty estimation which correlates with the absolute prediction error. The TTA approach is reproducible with different randomly generated data augmentations, establishing a promising framework for optimizing predictions of deep learning models. We believe there are broader implications of the proposed method for various fields reliant on accurate predictive data driven modeling.