期刊
APPLIED PHYSICS LETTERS
卷 115, 期 16, 页码 -出版社
AIP Publishing
DOI: 10.1063/1.5124529
关键词
-
资金
- National Natural Science Foundation of China [11432008, 11921002, 11620101001, 11672161]
Determining the macroscopic mechanical properties of composites with complex microstructures is a key issue in many of their applications. In this Letter, a machine learning-based approach is proposed to predict the effective elastic properties of composites with arbitrary shapes and distributions of inclusions. Using several data sets generated from the finite element method, a convolutional neural network method is developed to predict the effective Young's modulus and Poisson's ratio of composites directly from a window of their microstructural image. Through numerical experiments, we demonstrate that the trained network can efficiently provide an accurate mapping between the effective mechanical property and the microstructures of composites with complex structures. This study paves a way for characterizing heterogeneous materials in big data-driven material design. Published under license by AIP Publishing.
作者
我是这篇论文的作者
点击您的名字以认领此论文并将其添加到您的个人资料中。
推荐
暂无数据