摘要
Composites composed of two or more different materials are widely used in industrial fields because of their excellent mechanical properties. The analysis of multi-scale response of macroscopic composite structures requires a large amount of computations, which brings challenges to the development of efficient numerical methods. In recent years, the rapid development of artificial intelligence such as machine learning has created great opportunities for the efficient and accurate mechanical analysis of composite materials. But most mechanical surrogate models for multi-scale analysis of composite materials are purely data-driven, which lack physical interpretation. For the nonlinear mechanical response of the representative volume element of hyperelastic composites, three kinds of surrogate models are established based on data/physics-driven neural networks, employing different construction strategies to integrate physical interpretation into the models. By predicting the equivalent response of the representative volume element, the performance of the three models is analyzed,considering computational efficiency, accuracy and the range of applications. This work sheds more light on the establishment of effective surrogate models for the mechanical response of composites, balancing data and physics.
投稿的翻译标题 | Data/physics-driven surrogate models for the nonlinear mechanical response of composite materials |
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源语言 | 繁体中文 |
页(从-至) | 726-733 |
页数 | 8 |
期刊 | Jisuan Lixue Xuebao/Chinese Journal of Computational Mechanics |
卷 | 41 |
期 | 4 |
DOI | |
出版状态 | 已出版 - 8月 2024 |
关键词
- composite material
- data-driven modeling
- multi-scale analysis
- physics-driven modeling
- surrogate model