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Finite electro-elasticity with physics-augmented neural networks

Klein, Dominik K. ; Ortigosa, Rogelio ; Martínez-Frutos, Jesús ; Weeger, Oliver (2022)
Finite electro-elasticity with physics-augmented neural networks.
doi: 10.26083/tuprints-00021517
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Item Type: Report
Type of entry: Secondary publication
Title: Finite electro-elasticity with physics-augmented neural networks
Language: English
Date: 2022
Place of Publication: Darmstadt
Collation: 38 Seiten
DOI: 10.26083/tuprints-00021517
Corresponding Links:
Origin: Secondary publication service

In the present work, a machine learning based constitutive model for electro-mechanically coupled material behavior at finite deformations is proposed. Using different sets of invariants as inputs, an internal energy density is formulated as a convex neural network. In this way, the model fulfills the polyconvexity condition which ensures material stability, as well as thermodynamic consistency, objectivity, material symmetry, and growth conditions. Depending on the considered invariants, this physics-augmented machine learning model can either be applied for compressible or nearly incompressible material behavior, as well as for arbitrary material symmetry classes. The applicability and versatility of the approach is demonstrated by calibrating it on transversely isotropic data generated with an analytical potential, as well as for the effective constitutive modeling of an analytically homogenized, transversely isotropic rank-one laminate composite and a numerically homogenized cubic metamaterial. These examinations show the excellent generalization properties that physics-augmented neural networks offer also for multi-physical material modeling such as nonlinear electro-elasticity.

Uncontrolled Keywords: nonlinear electro-elasticity, constitutive modeling, physics-augmented machine learning, electro-active polymers, homogenization
Status: Preprint
URN: urn:nbn:de:tuda-tuprints-215179
Classification DDC: 600 Technology, medicine, applied sciences > 620 Engineering and machine engineering
Divisions: 16 Department of Mechanical Engineering > Cyber-Physical Simulation (CPS)
Exzellenzinitiative > Graduate Schools > Graduate School of Computational Engineering (CE)
Study Areas > Study Area Mechanic
Study Areas > Study area Computational Engineering
Date Deposited: 20 Jul 2022 12:10
Last Modified: 12 Apr 2023 08:05
URI: https://tuprints.ulb.tu-darmstadt.de/id/eprint/21517
PPN: 497909413
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