Introduction of a Recurrent Neural Network Constitutive Description within an Implicit Gradient Enhanced Damage Framework
Research output: Contribution to journal › Research article › Contributed › peer-review
Contributors
Abstract
The contribution at hand presents a method for the application of Recurrent neural network based constitutive models within a coupled field Finite Element Analysis. Thereby, an additional scalar field is coupled to the displacement field and evolved by a scalar variable at the integration points. By employing implicit gradient enhancement as regularization framework, the representation of softening material behavior is possible without the generation of localization phenomena. The feasibility of this approach is shown for an anisotropic constitutive description based on the {m}icroplane model, by employing a self-adversarial training scheme for enhanced robustness which was proposed to work for arbitrary inelastic materials. Its capabilities and limitations during application in Finite Element Analysis are evaluated and discussed for two numerical simulations.
Details
Original language | English |
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Article number | 107162 |
Number of pages | 14 |
Journal | Computers and Structures |
Volume | 289(2023) |
Early online date | 18 Sept 2023 |
Publication status | Published - Dec 2023 |
Peer-reviewed | Yes |
External IDs
Scopus | 85171373156 |
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Mendeley | d8c467b9-e88a-3e8c-9092-d4dfbe18cce7 |
Keywords
ASJC Scopus subject areas
Keywords
- Computational mechanics, Coupled field simulation, Machine learning, Multiscale modeling, Neural Network damage constitutive description