Applications in the field of data-driven mechanics are widely studied in the last years exploiting latest development of artificial intelligence. In this context, several machine learning techniques have been adopted to offer a fast and accurate prediction of the structural response of materials and complex structural systems. A relatively new machine learning concept relies on the use of Deep Operator Networks (DeepONets) that can approximate operators accurately and efficiently, from a relatively small dataset. The article, therefore, provides the methodology framework of applying a deep operator network (DeepONet) in structural mechanics applications. A dataset is developed using parametric non-linear finite element simulations for a two-dimensional fibre-reinforced composite structure. Then, a DeepONet is developed, aiming to predict the failure response of this structure. Comparison with results obtained from traditional Artificial Neural Networks (ANNs) is also presented. Results obtained from testing the trained DeepONet model on data not included in training indicate a proper performance. Testing the DeepONet model on unseen trunk input or branch input functions leads to satisfactory accuracy, while testing it on unseen trunk and branch input leads to a decent accuracy, that is improved compared with the one received from ANNs. Thus, the capacity of DeepONet to predict the response in the context of non-linear structural mechanics is evaluated.
Stavroulakis et al. (Fri,) studied this question.