多尺度力学的直接数据驱动算法

IF 6.9 1区工程技术 Q1 ENGINEERING, MULTIDISCIPLINARY

Computer Methods in Applied Mechanics and Engineering Pub Date : 2024-11-12 DOI:10.1016/j.cma.2024.117525

E. Prume , C. Gierden , M. Ortiz , S. Reese

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引用次数: 0

摘要

我们为多尺度力学问题提出了一种随机数据驱动求解器，它通过摆脱局部极小值和减少对度量参数的依赖来提高精确度，同时与非随机求解器相比，只需最小的改动。此外，我们还开发了一种自适应数据生成方案，以有效方式丰富数据集。这种丰富是通过利用材料切线信息和误差加权 k 均值聚类算法实现的。我们通过具有代表性的体积元素模型数据的三维测试案例对所提出的算法进行了评估。

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Direct data-driven algorithms for multiscale mechanics

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.

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来源期刊

Computer Methods in Applied Mechanics and Engineering 工程技术-工程：综合

CiteScore

12.70

自引率

15.30%

发文量

719

审稿时长

44 days

期刊介绍： Computer Methods in Applied Mechanics and Engineering stands as a cornerstone in the realm of computational science and engineering. With a history spanning over five decades, the journal has been a key platform for disseminating papers on advanced mathematical modeling and numerical solutions. Interdisciplinary in nature, these contributions encompass mechanics, mathematics, computer science, and various scientific disciplines. The journal welcomes a broad range of computational methods addressing the simulation, analysis, and design of complex physical problems, making it a vital resource for researchers in the field.