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An enhanced graph convolutional network-based method for predicting the mechanical behavior of lattice structures with random defects
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DOI：10.1016/j.tws.2026.115496.png)
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Abstract
En 中文
• An enhanced GCN-based method predicts mechanical behavior of defective lattices. • Randomly distributed defects are captured via structure-aware graph modeling. • The method generalizes well across unseen random defect configurations. • The proposed method serves as an efficient surrogate for defect-sensitive analysis.
Keywords:
Lattice structures
Random defect
Mechanical behavior prediction
Graph convolutional networks
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IF:
6.6
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1.1W
Citations:
4.0W
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