Parsimonious identification of a viscoelastic model using the mCRE framework and Lasso regularization

In this work, we investigate the parsimonious identification of linear viscoelastic models within the modified Constitutive Relation Error (mCRE) framework [1]. The proposed methodology leverages the robustness of mCRE for time-dependent materials and incorporates a Lasso-type regularization to promote sparsity in the identified parameters, following the rationale of the EUCLID framework introduced in [2]. A generalized Maxwell model serves as the reference formulation. Using synthetic or real data, the approach enables the extraction of a minimal set of active Maxwell branches from an overparameterized initialization and successfully recovers the underlying model structure. This contribution lays the groundwork for data-driven yet physics-informed identification strategies in rheological modeling. References : [1] Ludovic Chamoin, and Pierre Ladevèze, Model verification, updating, and selection from the Constitutive Relation Error concept, Advances in Applied Mechanics, Vol 59, 311-362, 2024. [2] Enzo Marino, Moritz Flaschel, Siddhant Kumar, and Laura De Lorenzis. Automated identification of linear viscoelastic constitutive laws with euclid. Mechanics of Materials, 181:104643, 2023. This project has received funding from the European Research Council (ERC) under the European Union’s Horizon 2020 research and innovation program under grant agreement No. 101002857.