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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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
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ISSN:0045-7825
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Abstract 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.
AbstractList 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.
ArticleNumber 117525
Author Gierden, C.
Ortiz, M.
Reese, S.
Prume, E.
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  surname: Reese
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  organization: Institute of Applied Mechanics, RWTH Aachen University, Mies-van-der-Rohe-Str. 1, D-52074 Aachen, Germany
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Keywords Computational mechanics
Algorithms
Data-Driven
Multiscale mechanics
Language English
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Snippet We propose a randomized data-driven solver for multiscale mechanics problems which improves accuracy by escaping local minima and reducing dependency on metric...
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SubjectTerms Algorithms
Computational mechanics
Data-Driven
Multiscale mechanics
Title Direct data-driven algorithms for multiscale mechanics
URI https://dx.doi.org/10.1016/j.cma.2024.117525
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