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Graph Neural Networks For Efficient Learning of Mechanical Properties of Polycrystals Jonathan M. Hestroffer Ebook Fully Accessible 2026 by kedsqzykh538 is a document available to read on EtoBox.

This document discusses the use of graph neural networks (GNNs) for efficiently predicting the mechanical properties of polycrystalline materials, specifically focusing on stiffness and yield strength of α-Ti microstructures. The GNN model demonstrates high accuracy in predictions, achieving mean relative errors of approximately 1% for known microstructures and less than 2% for unseen textures, outperforming traditional methods that require high-resolution data. The work highlights the advantages of GNNs in

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kedsqzykh538

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