  Local-nonlocal coupling approaches combine the computational efficiency of
local models and the accuracy of nonlocal models. However, the coupling process
is 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 local and nonlocal models should
be used in a coupling approach. This identification process uses the loading
functions and provides as output the selected model at the grid points.
Training is based on datasets 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 nonlocal model. 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 second approach consists of a window-based approach,
where loads are preprocessed and partitioned into windows and the problem is
formulated as a node-wise classification approach in which the central point of
each window is treated individually. The classification problems are solved via
deep learning algorithms based on convolutional neural networks. The
performance of these approaches is studied on one-dimensional numerical
examples using F1-scores and accuracy metrics. In particular, it is shown that
the windowing approach provides promising results, achieving an accuracy of
0.96 and an F1-score of 0.97. These results underscore the potential of the
approach to automate coupling processes, leading to more accurate and
computationally efficient solutions for material science applications.
