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Direct data-driven algorithms for multiscale mechanics
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DOI：10.1016/j.cma.2024.117525.png)
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Abstract
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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.
Keywords:
Data-Driven
Computational mechanics
Multiscale mechanics
Algorithms
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