Klein, Dominik K. ; Ortigosa, Rogelio ; Martínez-Frutos, Jesús ; Weeger, Oliver (2022)
Finite electro-elasticity with physics-augmented neural networks.
doi: 10.26083/tuprints-00021517
Report, Zweitveröffentlichung, Preprint
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Kurzbeschreibung (Abstract)
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.
| Typ des Eintrags: | Report |
|---|---|
| Erschienen: | 2022 |
| Autor(en): | Klein, Dominik K. ; Ortigosa, Rogelio ; Martínez-Frutos, Jesús ; Weeger, Oliver |
| Art des Eintrags: | Zweitveröffentlichung |
| Titel: | Finite electro-elasticity with physics-augmented neural networks |
| Sprache: | Englisch |
| Publikationsjahr: | 2022 |
| Ort: | Darmstadt |
| Publikationsdatum der Erstveröffentlichung: | 2022 |
| Kollation: | 38 Seiten |
| DOI: | 10.26083/tuprints-00021517 |
| URL / URN: | https://tuprints.ulb.tu-darmstadt.de/21517 |
| Zugehörige Links: | |
| Herkunft: | Zweitveröffentlichungsservice |
| Kurzbeschreibung (Abstract): | 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. |
| Freie Schlagworte: | nonlinear electro-elasticity, constitutive modeling, physics-augmented machine learning, electro-active polymers, homogenization |
| Status: | Preprint |
| URN: | urn:nbn:de:tuda-tuprints-215179 |
| Sachgruppe der Dewey Dezimalklassifikatin (DDC): | 600 Technik, Medizin, angewandte Wissenschaften > 620 Ingenieurwissenschaften und Maschinenbau |
| Fachbereich(e)/-gebiet(e): | Studienbereiche 16 Fachbereich Maschinenbau 16 Fachbereich Maschinenbau > Fachgebiet Cyber-Physische Simulation (CPS) Exzellenzinitiative Exzellenzinitiative > Graduiertenschulen Exzellenzinitiative > Graduiertenschulen > Graduate School of Computational Engineering (CE) Studienbereiche > Studienbereich Mechanik Studienbereiche > Studienbereich Computational Engineering |
| Hinterlegungsdatum: | 20 Jul 2022 12:10 |
| Letzte Änderung: | 21 Jul 2022 05:04 |
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