MACHINE LEARNING-BASED IDENTIFICATION OF THE INTERFACE REGIONS FOR COUPLING LOCAL AND NONLOCAL MODELS

Local-nonlocal coupling approaches provide a means to combine the computational efficiency of local models (LMs) and the accuracy of nonlocal models (NLMs). However, the coupling process can be challenging, requiring expertise to identify the interface between local and nonlocal regions. This study introduces a machine learning-based approach to automatically detect the regions in which the LM and NLM should be used in a coupling approach. This identification process takes as input the loading functions evaluated at the grid points and provides as output the selected model at those points. Training of the networks is based on datasets provided by classes of loading functions for which reference coupling configurations are computed using accurate coupled solutions, where accuracy is measured in terms of the relative error between the solution to the coupling approach and the solution to the NLM. We study two approaches that differ from one another in terms of the data structure. The first approach, referred to as the full-domain input data approach, inputs the full load vector and outputs a full label vector. In this case, the classification process is carried out globally. The seco