An Artificial Neural Network Based Solution Scheme for Periodic Computational Homogenization of Electrostatic Problems

• Published in: Mathematical and computational applications Vol. 24; no. 2; p. 40
• Main Authors: Göküzüm, Felix Selim, Nguyen, Lu Trong Khiem, Keip, Marc-André
• Format: Journal Article
• Language: English
• Published: MDPI AG 01-06-2019
• Subjects: artificial neural networks; computational homogenization; machine learning
• ISSN: 2297-8747; 2297-8747
• DOI: 10.3390/mca24020040

The present work addresses a solution algorithm for homogenization problems based on an artificial neural network (ANN) discretization. The core idea is the construction of trial functions through ANNs that fulfill a priori the periodic boundary conditions of the microscopic problem. A global potential serves as an objective function, which by construction of the trial function can be optimized without constraints. The aim of the new approach is to reduce the number of unknowns as ANNs are able to fit complicated functions with a relatively small number of internal parameters. We investigate the viability of the scheme on the basis of one-, two- and three-dimensional microstructure problems. Further, global and piecewise-defined approaches for constructing the trial function are discussed and compared to finite element (FE) and fast Fourier transform (FFT) based simulations.