Micrometer: Micromechanics Transformer for Predicting Mechanical Responses of Heterogeneous Materials

• URL: http://arxiv.org/abs/2410.05281v1
• Date: Mon, 23 Sep 2024 16:01:37 GMT
• Title: Micrometer: Micromechanics Transformer for Predicting Mechanical Responses of Heterogeneous Materials
• Authors: Sifan Wang, Tong-Rui Liu, Shyam Sankaran, Paris Perdikaris,
• Abstract summary: We introduce the Micromechanics Transformer (em Micrometer), an artificial intelligence framework for predicting the mechanical response of heterogeneous materials. Micrometer can achieve state-of-the-art performance in predicting microscale strain fields across a wide range of microstructures. Our work represents a significant step towards AI-driven innovation in computational solid mechanics.
• Score: 4.759109475818876
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: Heterogeneous materials, crucial in various engineering applications, exhibit complex multiscale behavior, which challenges the effectiveness of traditional computational methods. In this work, we introduce the Micromechanics Transformer ({\em Micrometer}), an artificial intelligence (AI) framework for predicting the mechanical response of heterogeneous materials, bridging the gap between advanced data-driven methods and complex solid mechanics problems. Trained on a large-scale high-resolution dataset of 2D fiber-reinforced composites, Micrometer can achieve state-of-the-art performance in predicting microscale strain fields across a wide range of microstructures, material properties under any loading conditions and We demonstrate the accuracy and computational efficiency of Micrometer through applications in computational homogenization and multiscale modeling, where Micrometer achieves 1\% error in predicting macroscale stress fields while reducing computational time by up to two orders of magnitude compared to conventional numerical solvers. We further showcase the adaptability of the proposed model through transfer learning experiments on new materials with limited data, highlighting its potential to tackle diverse scenarios in mechanical analysis of solid materials. Our work represents a significant step towards AI-driven innovation in computational solid mechanics, addressing the limitations of traditional numerical methods and paving the way for more efficient simulations of heterogeneous materials across various industrial applications.

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