Evidential neural network for tensile stress uncertainty quantification in thermoplastic elastomers

This work presents the use of artificial neural networks (ANNs) with deep evidential regression to model the tensile stress response of a thermoplastic elastomer (TPE) considering uncertainty. Three Gaussian noise scenarios were added to a previous dataset of a TPE to simulate noise in the stress response. The trained ANN models were able to address stress–strain data that were not used for their training or validation, even in the presence of noise. The uncertainty in all tested ANN scenarios comprised, within ± \(3\sigma\) $3\sigma$ , the noisy data of the TPE stress response. The method was extended to other grades of Hytrel material with ANN architectures that obtained results with a coefficient of determination of about 0.9. These results suggest that shallow neural networks, equipped and trained using evidential output layers and an evidential regression loss, can predict, generalize, and simulate noisy tensile stress responses in TPE materials.