Automated identification of linear viscoelastic constitutive laws with EUCLID

Marino, Enzo; Flaschel, Moritz; Kumar, Siddhant; Lorenzis, Laura De

doi:10.1016/j.mechmat.2023.104643

Cited by 18 publications

(2 citation statements)

References 42 publications

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“…With more advanced measurement technologies such as Digital Image Correlation (DIC) and Digital Volume Correlation (DVC), it is possible to obtain local information contained in full-field displacement/deformation data [74]. The use of DIC measurements in dynamic experimental tests can be subject to quality problems such as loss of grid points during loading, inaccessible measurements close to the boundary, and non-Gaussian noise.…”

Section: Models Of Ve Materials and Identification Of Theirs Parametersmentioning

confidence: 99%

Dynamics of Structures, Frames, and Plates with Viscoelastic Dampers or Layers: A Literature Review

Lewandowski,

Litewka,

Łasecka-Plura

et al. 2023

Buildings

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The paper is devoted to a review of recent achievements in the field of dynamic analysis of structures and structural elements, such as beams and plates, with embedded viscoelastic (VE) dampers and/or layers. The general characteristics of VE materials, their rheological models, and methods of parameters identification are discussed. New formulations of dynamic problems for systems with VE elements are also reviewed. The methods of determination of dynamic characteristics, together with the methods of analysis of steady-state and transient vibrations of such systems, are also discussed. Both linear and geometrically non-linear vibrations are considered. The paper ends with a review of the methods of sensitivity and uncertainty analysis, and the methods of optimization, for structures with VE elements.

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Section: Models Of Ve Materials and Identification Of Theirs Parametersmentioning

confidence: 99%

Dynamics of Structures, Frames, and Plates with Viscoelastic Dampers or Layers: A Literature Review

Lewandowski,

Litewka,

Łasecka-Plura

et al. 2023

Buildings

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“…EUCLID can automatically discover the target model from a large library of interpretable candidate material models, with the input of full-field displacements and global reaction forces instead of labeled stress-strain data pairs. Until now, EUCLID has been successfully demonstrated for hyperelasticity (Flaschel et al, 2021c,b,a;Joshi et al, 2022;Flaschel et al, 2023b), viscoelasticity (Marino et al, 2023), pressure-insensitive associated plasticity (Flaschel et al, 2022a,c,b), and generalized standard material models (Flaschel et al, 2023a).…”

Section: Introductionmentioning

confidence: 99%

Discovering non-associated pressure-sensitive plasticity models with EUCLID

Xu,

Flaschel,

De Lorenzis

2024

Preprint

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We extend EUCLID (Efficient Unsupervised Constitutive Law Identification and Discovery) – a data-driven framework for automated material model discovery – to pressure-sensitive plasticity models, encompassing arbitrarily shaped yield surfaces with convexity constraints and non-associated flow rules. The method only requires full-field displacement and boundary force data from one single experiment and delivers constitutive laws as interpretable mathematical expressions. We construct a material model library for pressure-sensitive plasticity models with nonassociated flow rules in four steps: (1) a Fourier series describes an arbitrary yield surface shape in the deviatoric stress plane; (2) a pressure-sensitive term in the yield function defines the shape of the shear failure surface and determines plastic deformation under tension; (3) a compression cap term determines plastic deformation under compression; (4) a non-associated flow rule may be adopted to avoid the excessive dilatancy induced by plastic deformations. In contrast to traditional parameter identification methods, EUCLID is equipped with a sparsity promoting regularization to restrain the number of model parameters (and thus modeling features) to the minimum needed to accurately interpret the data, thus achieving a compromise between model simplicity and accuracy. The convexity of the learned yield surface is guaranteed by a set of constraints in the inverse optimization problem. We demonstrate the proposed approach in multiple numerical experiments with noisy data, and show the ability of EUCLID to accurately select a suitable material model from the starting library.

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