Machine Learning Aided Multiscale Magnetostatics

TL;DR

This paper introduces a CNN-based approach for multiscale magnetostatic modeling that achieves high accuracy and reduces computational costs in predicting effective permeability of heterogeneous materials.

Contribution

It develops a CNN model trained on microstructure images to efficiently predict apparent permeability, improving speed while maintaining accuracy in multiscale magnetostatic simulations.

Findings

CNN achieves high accuracy in permeability prediction.

Significant reduction in computation time.

Effective in 2D and 3D microstructure modeling.

Abstract

Computational material modeling using advanced numerical techniques speeds up the design process and reduces the costs of developing new engineering products. In the field of multiscale modeling, huge computation efforts are expected for modeling heterogeneous materials while trying to reach high accuracy levels. In this work, a machine learning approach, namely the convolutional neural network (CNN), is developed as a solution providing a high level of accuracy, while being computationally efficient. The input for the CNN model consists of two-/three-dimensional images of artificial periodic and biphasic microstructures in the form of nonoverlapping and overlapping, mono- and polydisperse circular/spherical disk systems, which are generated by a random sequential inhibition process. These correspond to Statistical Volume Elements (SVE). Considering linear magnetostatics at the microscale, the output is the apparent permeability of the SVE. Training and testing data for the apparent properties is produced with finite element method-based two-scale asymptotic homogenization. The model efficiency is revealed by employing some representative examples in two- and three-dimensional settings. In this regard, the performance of the CNN model is assessed with the applied computational homogenization method relating to the accuracy and computational efficiency. The results with the CNN model show high accuracy in predicting the homogenized permeability and a significant decrease in computation time.