Decoding microstructure-property relationships in polycrystals: an explainable GNN surrogate for crystal plasticity
In this work, a surrogate modeling approach based on graph neural networks (GNNs) is presented to rapidly predict the homogenized stress-strain response of polycrystalline microstructure volume elements (MVEs). Each grain is represented as a node in a graph, with features such as crystallographic orientation, grain siz...
A. Safi, Pedro Campos, Frederic E. Bock et al.
· Machine Learning for Computa... · 0 citations