Reduced-order structure–property (S-P) linkages play a pivotal role in the tailored design of materials for advanced engineering components. There is a critical need to distill these from the simulation datasets aggregated using sophisticated, computationally expensive, physics-based numerical tools (e.g., finite element methods). The recent emergence of materials data science approaches has opened new avenues for addressing this challenge. In this paper, we critically compare the relative merits of the application of four distinct machine learning approaches for their efficacy in extracting microstructure-property linkages from the finite element simulation data aggregated on high-contrast elastic composites with different microstructures. The machine learning approaches selected for the study have included different combinations of local/global and parametric/nonparametric approaches. Furthermore, the nonparametric approaches selected for this study are based on Gaussian Process (GP) models that allow for a formal treatment of uncertainty quantification in the predicted values. The predictive performances of these different approaches have been compared against each other using rigorous cross-validation error metrics. Furthermore, their sensitivity to both the dataset size and dimensionality has been investigated.

中文翻译：

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局部/全局和参数/非参数机器学习方法在高对比度3D弹性复合材料中建立结构-属性链接的功效的比较研究

降序结构-属性（SP）链接在高级工程组件的材料定制设计中起着关键作用。迫切需要从使用复杂的，计算量大的，基于物理的数值工具（例如，有限元方法）汇总的模拟数据集中提取这些数据。材料数据科学方法的最新出现为应对这一挑战开辟了新途径。在本文中，我们严格地比较了四种不同的机器学习方法在从不同结构的高对比度弹性复合材料上聚集的有限元模拟数据中提取微结构-性能链接的功效方面的相对优势。为该研究选择的机器学习方法包括局部/全局方法和参数/非参数方法的不同组合。此外，为这项研究选择的非参数方法基于高斯过程（GP）模型，该模型允许对预测值中的不确定性量化进行正式处理。这些不同方法的预测性能已使用严格的交叉验证误差指标进行了比较。此外，已经研究了它们对数据集大小和维度的敏感性。这些不同方法的预测性能已使用严格的交叉验证误差指标进行了比较。此外，已经研究了它们对数据集大小和维度的敏感性。这些不同方法的预测性能已使用严格的交叉验证误差指标进行了比较。此外，已经研究了它们对数据集大小和维度的敏感性。