期刊
COMPUTER METHODS IN APPLIED MECHANICS AND ENGINEERING
卷 360, 期 -, 页码 -出版社
ELSEVIER SCIENCE SA
DOI: 10.1016/j.cma.2019.112693
关键词
Multiscale; Composites; Bayesian inference; Neural Network; Non-linear
资金
- Walloon Region, Belgium [1410246-STOMMMAC (CT-INT 2013-03-28)]
- Gaitek 2015 program of the Basque Government, Spain
- Austrian Research Promotion Agency (ffg), Austria [850392]
We develop a Bayesian Inference (BI) of the parameters of a non-linear multiscale model and of its material constitutive laws using experimental composite coupon 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 (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. (C) 2019 Elsevier B.V. All rights reserved.
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