A multi-fidelity data-driven model for highly accurate and computationally efficient modeling of short fiber composites

To develop physics-based models and establish a structure-property relationship for short fiber composites, there are a wide range of micro-structural properties to be considered. To achieve a high accuracy, high-fidelity full-field simulations are required. These simulations are computationally very expensive, and any single analysis could potentially take days to finish. A solution for this issue is to develop surrogate models using artificial neural networks. However, generating a high-fidelity data set requires a huge amount of time. To solve this problem, we used transfer learning technique, a limited amount of high-fidelity full-field simulations, together with a previously developed recurrent neural network model trained on low-fidelity mean-field data. The new RNN model has a very high accuracy (in comparison with full-field simulations) and is remarkably efficient. This model can be used not only for highly efficient modeling purposes, but also for designing new short fiber composites.

Automated summary

The aim of this study is to develop physics-based models and establish a structure-property relationship for short fiber composites. High-fidelity full-field simulations are computationally expensive and time-consuming, so the use of artificial neural networks and transfer learning technique is proposed to solve this issue and improve modeling accuracy and efficiency.