5 citations · 5 across the 4 of their papers we have counts for
4 papers
A comparative study of transformer models and recurrent neural networks for path-dependent composite materials
Petter Uvdal, Mohsen Mirkhalaf
Accurate modeling of Short Fiber Reinforced Composites (SFRCs) remains computationally expensive for full-field simulations. Data-driven surrogate models using Artificial Neural Ne…
Test-time data augmentation: improving predictions of recurrent neural network models of composites
Petter Uvdal, Mohsen Mirkhalaf
Recurrent Neural Networks (RNNs) have emerged as an interesting alternative to conventional material modeling approaches, particularly for nonlinear path dependent materials. Remar…
A novel Taguchi-based approach for optimizing neural network architectures: application to elastic short fiber composites
Mohammad Hossein Nikzad, Mohammad Heidari-Rarani, Mohsen Mirkhalaf
This study presents an innovative application of the Taguchi design of experiment method to optimize the structure of an Artificial Neural Network (ANN) model for the prediction of…
Augmentation of scarce data -- a new approach for deep-learning modeling of composites
Hon Lam Cheung, Petter Uvdal, Mohsen Mirkhalaf
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 da…