Towards trustworthy AI in materials mechanics through domain-guided attention

Ensuring the trustworthiness and robustness of deep learning models remains a fundamental challenge, particularly in high-stakes scientific applications. In this study, we present a framework called attention-guided training that combines explainable artificial intelligence techniques with quantitative evaluation and domain-specific priors to guide model attention. We demonstrate that domain specific feedback on model explanations during training can enhance the model's generalization capabilities. We validate our approach on the task of semantic crack tip segmentation in digital image correlation data which is a key application in the fracture mechanical characterization of materials. By aligning model attention with physically meaningful stress fields, such as those described by Williams' analytical solution, attention-guided training ensures that the model focuses on physically relevant regions. This finally leads to improved generalization and more faithful explanations.

Physics-guided adversarial networks for artificial digital image correlation data generation

Digital image correlation (DIC) has become a valuable tool in the evaluation of mechanical experiments, particularly fatigue crack growth experiments. The evaluation requires accurate information of the crack path and crack tip position, which is difficult to obtain due to inherent noise and artefacts. Machine learning models have been extremely successful in recognizing this relevant information given labelled DIC displacement data. For the training of robust models, which generalize well, big data is needed. However, data is typically scarce in the field of material science and engineering because experiments are expensive and time-consuming. We present a method to generate synthetic DIC displacement data using generative adversarial networks with a physics-guided discriminator. To decide whether data samples are real or fake, this discriminator additionally receives the derived von Mises equivalent strain. We show that this physics-guided approach leads to improved results in terms of visual quality of samples, sliced Wasserstein distance, and geometry score.