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A machine learning-aided robust topology optimization method for the design of auxetic metamaterials
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DOI：10.1016/j.cma.2025.118577.png)
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Abstract
En 中文
• A machine learning-aided robust topology optimization framework is proposed. • Auxetic metamaterials are designed to achieve robustness under material uncertainties. • Machine learning methods are embedded to estimate statistical features efficiently. • The framework supports diverse base material uncertainties and distributions. • The framework promotes manufacturable design by avoiding fine-scale structures.
Keywords:
Robust topology optimization
Machine learning
Auxetic metamaterials
Negative Poisson’s ratio
Material uncertainty
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