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 t...

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Vydané v:	Computer methods in applied mechanics and engineering Ročník 433; s. 117525
Hlavní autori:	Prume, E., Gierden, C., Ortiz, M., Reese, S.
Médium:	Journal Article
Jazyk:	English
Vydavateľské údaje:	Elsevier B.V 01.01.2025
Predmet:	Algorithms Computational mechanics Data-Driven Multiscale mechanics Computational mechanics Algorithms Data-Driven Multiscale mechanics
ISSN:	0045-7825
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Popis
Shrnutí:	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.
ISSN:	0045-7825
DOI:	10.1016/j.cma.2024.117525