Application of artificial intelligence and machine learning in peridynamics

With the advancement of sensor technologies, data analysis, and computational resources, data-driven models have become important in many different scientific fields. However, for complex problems without having sufficient number of data, the accuracy of the data-driven approaches deteriorates. As an alternative, theory-guided data science which is a combination of physics-driven and data-driven models is a promising approach. In this chapter, a peridynamic machine learning approach is presented based on linear regression. To demonstrate the capability of the coupled peridynamic machine learning approach, four different numerical examples are considered including one-dimensional bar subjected to axial loading, vibration of a one-dimensional bar, two-dimensional plate subjected to tension loading and two-dimensional plate with a pre-existing crack subjected to tension loading.