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

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 fro...