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Abstract Predicting the full-range stress–strain behavior of architected elastomers is challenging due to limited data and complex deformation mechanisms. We propose a physics-informed multimodal machine learning framework that embeds domain knowledge at both the data and model levels. At the data level, the Gibson–Ashby law guides targeted augmentation, and energy absorption efficiency segments the curves into distinct stages.
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