An indirect training approach for implicit constitutive modelling using recurrent neural networks and the virtual fields method
From MaRDI portal
Publication:6497200
DOI10.1016/J.CMA.2024.116961MaRDI QIDQ6497200
Petia Georgieva, Rúben Lourenço, Elias Cueto, António Andrade-Campos
Publication date: 6 May 2024
Published in: Computer Methods in Applied Mechanics and Engineering (Search for Journal in Brave)
recurrent neural networkartificial neural networkvirtual fields methodgated-recurrent unitimplicit constitutive modelindirect training
Cites Work
- Unnamed Item
- Unnamed Item
- Learning constitutive relations from indirect observations using deep neural networks
- Applying the virtual fields method to the identification of elasto-plastic constitutive parameters
- Artificial neural network as an incremental nonlinear constitutive model for a finite element code.
- Sensitivity-based virtual fields for the non-linear virtual fields method
- Multilayer feedforward networks are universal approximators
- Learning constitutive relations using symmetric positive definite neural networks
- Accelerating multiscale finite element simulations of history-dependent materials using a recurrent neural network
- Learning viscoelasticity models from indirect data using deep neural networks
- \(\mathrm{SO}(3)\)-invariance of informed-graph-based deep neural network for anisotropic elastoplastic materials
- Automated discovery of generalized standard material models with EUCLID
- Neural network constitutive modelling for non-linear characterization of anisotropic materials
- Finite-element-model Updating Using Computional Intelligence Techniques
- Numerical implementation of a neural network based material model in finite element analysis
- Advanced discretization techniques for hyperelastic physics-augmented neural networks
This page was built for publication: An indirect training approach for implicit constitutive modelling using recurrent neural networks and the virtual fields method
