We propose a randomized data-driven solver for multiscale mechanics problems which improves accuracy by escaping local minima and reducing dependency on metric parameters, while requiring minimal changes relative to non-randomized solvers. We additionally develop an adaptive data-generation scheme to enrich data sets in an effective manner. This enrichment is achieved by utilizing material tangent information and an error-weighted k-means clustering algorithm. The proposed algorithms are assessed by means of three-dimensional test cases with data from a representative volume element model.

中文翻译：

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用于多尺度力学的直接数据驱动算法


我们提出了一种用于多尺度力学问题的随机数据驱动求解器，它通过逃逸局部最小值和减少对度量参数的依赖来提高准确性，同时相对于非随机求解器需要最小的更改。我们还开发了一种自适应数据生成方案，以有效地丰富数据集。这种丰富是通过利用材料切线信息和误差加权 k-means 聚类算法来实现的。所提出的算法通过三维测试用例和来自代表性体积单元模型的数据进行评估。