Automated identification of linear viscoelastic constitutive laws with EUCLID

Automated identification of linear viscoelastic constitutive laws with EUCLID

Marino, Enzo (University of Florence)
Flaschel, Moritz (ETH Zürich)
Kumar, Siddhant (TU Delft Team Sid Kumar)
De Lorenzis, Laura (ETH Zürich)

2023

We extend EUCLID, a computational strategy for automated material model discovery and identification, to linear viscoelasticity. For this case, we perform a priori model selection by adopting a generalized Maxwell model expressed by a Prony series, and deploy EUCLID for identification. The methodology is based on four ingredients: i. full-field displacement and net force data; ii. a very wide material model library — in our case, a very large number of terms in the Prony series; iii. the linear momentum balance constraint; iv. the sparsity constraint. The devised strategy comprises two stages. Stage 1 relies on sparse regression; it enforces momentum balance on the data and exploits sparsity-promoting regularization to drastically reduce the number of terms in the Prony series and identify the material parameters. Stage 2 relies on k-means clustering; starting from the reduced set of terms from stage 1, it further reduces their number by grouping together Maxwell elements with very close relaxation times and summing the corresponding moduli. Automated procedures are proposed for the choice of the regularization parameter in stage 1 and of the number of clusters in stage 2. The overall strategy is demonstrated on artificial numerical data, both without and with the addition of noise, and shown to efficiently and accurately identify a linear viscoelastic model with five relaxation times across four orders of magnitude, out of a library with several hundreds of terms spanning relaxation times across seven orders of magnitude.

k-means clustering
Lasso regularization
Linear viscoelasticity
Sparse regression
Unsupervised learning

http://resolver.tudelft.nl/uuid:8601bf31-4eb3-4aef-90ff-d6a9760531b9

https://doi.org/10.1016/j.mechmat.2023.104643

2023-10-01

0167-6636

Mechanics of Materials, 181

Green Open Access added to TU Delft Institutional Repository 'You share, we take care!' - Taverne project https://www.openaccess.nl/en/you-share-we-take-care Otherwise as indicated in the copyright section: the publisher is the copyright holder of this work and the author uses the Dutch legislation to make this work public.

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© 2023 Enzo Marino, Moritz Flaschel, Siddhant Kumar, Laura De Lorenzis