An artificial neural network for surrogate modeling of stress fields in viscoplastic polycrystalline materials

• Published in: npj computational materials Vol. 9; no. 1; pp. 37 - 10
• Main Authors: Khorrami, Mohammad S., Mianroodi, Jaber R., Siboni, Nima H., Goyal, Pawan, Svendsen, Bob, Benner, Peter, Raabe, Dierk
• Format: Journal Article
• Language: English
• Published: London Nature Publishing Group UK 13.03.2023; Nature Publishing Group; Nature Portfolio
• Subjects: 639/301/1023/303; 639/301/1034/1037; Artificial neural networks; Boundary conditions; Boundary value problems; Characterization and Evaluation of Materials; Chemistry and Materials Science; Computational Intelligence; Control theory; Datasets; Equilibrium; Grain boundaries; Grain size; Materials Science; Mathematical and Computational Engineering; Mathematical and Computational Physics; Mathematical Modeling and Industrial Mathematics; Mathematical models; Mechanical analysis; Mechanics; Microstructure; Neural networks; Spectral methods; Stress distribution; Theoretical
• ISSN: 2057-3960; 2057-3960
• DOI: 10.1038/s41524-023-00991-z

The purpose of this work is the development of a trained artificial neural network for surrogate modeling of the mechanical response of elasto-viscoplastic grain microstructures. To this end, a U-Net-based convolutional neural network (CNN) is trained using results for the von Mises stress field from the numerical solution of initial-boundary-value problems (IBVPs) for mechanical equilibrium in such microstructures subject to quasi-static uniaxial extension. The resulting trained CNN (tCNN) accurately reproduces the von Mises stress field about 500 times faster than numerical solutions of the corresponding IBVP based on spectral methods. Application of the tCNN to test cases based on microstructure morphologies and boundary conditions not contained in the training dataset is also investigated and discussed.