Introduction of a Recurrent Neural Network Constitutive Description within an Implicit Gradient Enhanced Damage Framework

Research output: Contribution to journalResearch articleContributedpeer-review


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.


Original languageEnglish
Article number107162
JournalComputers and Structures
Issue number289
Early online date18 Sept 2023
Publication statusPublished - Dec 2023

External IDs

Scopus 85171373156
Mendeley d8c467b9-e88a-3e8c-9092-d4dfbe18cce7



  • Computational mechanics, Coupled field simulation, Machine learning, Multiscale modeling, Neural Network damage constitutive description