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#graph neural networks Open access Sep 2026

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. · 0 citations

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