There has been a growing recognition of the opportunities afforded by advanced data science and informatics approaches in addressing the computational demands of modeling and simulation of multiscale materials science phenomena. More specifically, the mining of microstructure–property relationships by various methods in machine learning and data mining opens exciting new opportunities that can potentially result in a fast and efficient material design. This work explores and presents multiple viable approaches for computationally efficient predictions of the microscale elastic strain fields in a three-dimensional (3-D) voxel-based microstructure volume element (MVE). Advanced concepts in machine learning and data mining, including feature extraction, feature ranking and selection, and regression modeling, are explored as data experiments. Improvements are demonstrated in a gradually escalated fashion achieved by (1) feature descriptors introduced to represent voxel neighborhood characteristics, (2) a reduced set of descriptors with top importance, and (3) an ensemble-based regression technique.

中文翻译：

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高对比度复合材料中弹性局部连接的机器学习方法。

人们越来越认识到高级数据科学和信息学方法在解决多尺度材料科学现象的建模和仿真的计算需求方面所提供的机会。更具体地说，通过机器学习和数据挖掘中的各种方法来挖掘微观结构与属性的关系，带来了令人兴奋的新机遇，这些机遇有可能导致快速高效的材料设计。这项工作探索并提出了多种可行的方法，用于基于三维（3-D）体素的微结构体元素（MVE）中的微尺度弹性应变场的高效计算预测。作为数据实验，探索了机器学习和数据挖掘中的高级概念，包括特征提取，特征排名和选择以及回归建模。