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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Bibliographic Details
Published in:Computer methods in applied mechanics and engineering Vol. 433; p. 117525
Main Authors: Prume, E., Gierden, C., Ortiz, M., Reese, S.
Format: Journal Article
Language:English
Published: Elsevier B.V 01.01.2025
Subjects:
Algorithms
Computational mechanics
Data-Driven
Multiscale mechanics
Computational mechanics
Algorithms
Data-Driven
Multiscale mechanics
ISSN:0045-7825
Online Access:Get full text
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Summary: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