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Two-scale topology optimization of multiscale structures and materials via deep neural networks for local and global buckling
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DOI：10.1016/j.cma.2026.119038.png)
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Abstract
En 中文
• Proposes a DNN-assisted two-scale framework for buckling topology design. • Uses a neural surrogate to predict homogenized properties. • Controls local and global buckling using a worst-case model. • Achieves about 104×speedup over direct FE-based analysis. • Handles macro–micro buckling, compliance, and two-scale volume limits.
Keywords:
Topology optimization
Two-scale
Buckling strength
Stability
Lattice microstructure
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