A single trained TACS-GNN-ODMN framework builds accurate, physics-interpretable surrogate models for unseen polycrystalline microstructures without retraining, predicting stress-strain response (≤2% mean error) and texture evolution at 224-281× speed-up.
On the micromechanics of deep material networks
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A Parametric Multiscale Surrogate Framework Based on Texture-Generalizable Deep Material Networks for Polycrystal Modeling
A single trained TACS-GNN-ODMN framework builds accurate, physics-interpretable surrogate models for unseen polycrystalline microstructures without retraining, predicting stress-strain response (≤2% mean error) and texture evolution at 224-281× speed-up.