Introduction of a recurrent neural network constitutive description within an implicit gradient enhanced damage framework
Julien Philipp Stöcker, Jakob Platen, Michael Kaliske
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 microplane 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.