NOTICE: this is the author’s version of a work that was accepted for publication in Computer Methods in Applied Mechanics and Engineering. Changes resulting from the publishing process, such as peer review, editing, corrections, structural formatting, and other quality control mechanisms may not be reflected in this document. Changes may have been made to this work since it was submitted for publication. A definitive version was subsequently published in Computer Methods in Applied Mechanics and Engineering 360 (2020) 112693, DOI: 10.1016/j.cma.2019.112693
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[en] We develop a Bayesian Inference (BI) of a non-linear multiscale model and material parameters using experimental composite coupons tests as observation data. In particular we consider non-aligned Short Fibers Reinforced Polymer (SFRP) as a composite material system and Mean-Field Homogenization (MFH) as a multiscale model. Although MFH is computationally efficient, when considering non-aligned inclusions, the evaluation cost of a non-linear response for a given set of model and material parameters remains too prohibitive to be coupled with the sampling process required by the BI. Therefore, a Neural-Network-type (NNW) is first trained using the MFH model, and is then used as a surrogate model during the BI process, making the identification process affordable.
H2020 - 685451 - M-ERA.NET 2 - ERA-NET for materials research and innovation
Name of the research project :
The research has been funded by the Walloon Region under the agreement no 1410246-STOMMMAC (CT-INT 2013-03-28), by the Gaitek 2015 programm of the Basque Government, and by the Austrian Research Promotion Agency (ffg) under the agreement no 850392 (STOMMMAC) in the context of the M-ERA.NET Joint Call 2014.
Funders :
DG RDT - Commission Européenne. Direction Générale de la Recherche et de l'Innovation CE - Commission Européenne
Commentary :
Data can be downloaded on https://gitlab.uliege.be/moammm/moammmpublic/tree/master/publicationsData/2020_CMAME_BI_NNW
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