A comparative study of transformer models and recurrent neural networks for path-dependent composite materials

TL;DR

This study compares transformer models and RNNs for modeling the response of fiber-reinforced composites, highlighting their accuracy, scalability, and inference speed differences in various data regimes.

Contribution

It provides a systematic comparison of transformers and RNNs for path-dependent composite modeling, including hyperparameter tuning and analysis of scaling laws.

Findings

Transformers are faster at inference, requiring 0.5 ms per prediction.

RNNs outperform transformers on small datasets and in extrapolation.

Transformers perform comparably on large datasets in terms of accuracy.

Abstract

Accurate modeling of Short Fiber Reinforced Composites (SFRCs) remains computationally expensive for full-field simulations. Data-driven surrogate models using Artificial Neural Networks (ANNs) have been proposed as an efficient alternative to numerical modeling, where Recurrent Neural Networks (RNNs) are increasingly being used for path-dependent multiscale modeling by predicting the homogenized response of a Representative Volume Element (RVE). However, recently, transformer models have been developed and they offer scalability and efficient parallelization, yet have not been systematically compared with RNNs in this field. In this study, we perform a systematic comparison between RNNs and transformer models trained on sequences of homogenized response of SFRC RVEs. We study the effect on two types of hyperparameters, namely architectural hyperparameters (such as the number of GRU layers, hidden size, number of attention heads, and encoder blocks) and training hyperparameters (such as learning rate and batch size). Both sets of hyperparameters are tuned using Bayesian optimization. We then analyze scaling laws with respect to dataset size and inference accuracy in interpolation and extrapolation regimes. The results show that while transformer models remain competitive in terms of accuracy on large datasets, the RNNs demonstrate better accuracy on small datasets and show better extrapolation performance. Furthermore, under extrapolation, there is a clear difference, where the RNN remains accurate, while the transformer model performs poorly. On the other hand, the transformer model is 7 times faster at inference, requiring 0.5 ms per prediction compared to the 3.5 ms per prediction for the RNN model.