ML-based identification of the interface regions for coupling local and nonlocal models

• URL: http://arxiv.org/abs/2404.15388v1
• Date: Tue, 23 Apr 2024 14:19:36 GMT
• Title: ML-based identification of the interface regions for coupling local and nonlocal models
• Authors: Noujoud Nader, Patrick Diehl, Marta D'Elia, Christian Glusa, Serge Prudhomme,
• Abstract summary: Local-nonlocal coupling approaches combine the computational efficiency of local models and the accuracy of nonlocal models. This study introduces a machine learning-based approach to automatically detect the regions in which the local and nonlocal models should be used.
• Score: 0.0
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

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