The simulation of complex engineering structures built from magneto-rheological elastomers is a computationally challenging task. Using the FE 2 method, which is based on computational homogenisation, leads to the repetitive solution of micro-scale FE problems, causing excessive computational effort. In this paper, the micro-scale FE problems are replaced by POD reduced models of comparable accuracy. As these models do not deliver the required reductions in computational effort, they are combined with hyper-reduction methods like the Discrete Empirical Interpolation Method (DEIM), Gappy POD, Gauss–Newton Approximated Tensors (GNAT), Empirical Cubature (EC) and Reduced Integration Domain (RID). The goal of this work is the comparison of the ...
In this work presents a strategy to diminish the computational cost of a hierarchical (FE2) multi-sc...
The authors have shown in previous contributions that reduced order modeling with optimal cubature a...
In this paper, we propose upper and lower error bounding techniques for reduced order modelling appl...
The simulation of complex engineering structures built from magneto-rheological elastomers is a comp...
The predictive simulation of elaborate engineering structures based on magneto-active polymers is c...
Computing the macroscopic material response of a continuum body commonly involves the formulation of...
A novel model-order reduction technique for the solution of the fine-scale equilibrium problem appea...
A High-Performance Reduced-Order Model (HPROM) technique, previously presented by the authors in the...
In recent years, there has been a growing interest in understanding complex microstructures and thei...
Proper Orthogonal Decomposition (POD) has been successfully used to reduce the size of linear Finite...
In the electrical engineering field, numerical simulation allows to avoid experiments which can be e...
Model Order Reduction (MOR) methods are used to cope with high computational costs typically involve...
Computational numerical methods are important tools in science and technology today. Numerical simul...
Numerical simulations currently represent one of the most efficient ways of studying complex physica...
The authors have shown in previous contributions that reduced order modeling with optimal cubature a...
In this work presents a strategy to diminish the computational cost of a hierarchical (FE2) multi-sc...
The authors have shown in previous contributions that reduced order modeling with optimal cubature a...
In this paper, we propose upper and lower error bounding techniques for reduced order modelling appl...
The simulation of complex engineering structures built from magneto-rheological elastomers is a comp...
The predictive simulation of elaborate engineering structures based on magneto-active polymers is c...
Computing the macroscopic material response of a continuum body commonly involves the formulation of...
A novel model-order reduction technique for the solution of the fine-scale equilibrium problem appea...
A High-Performance Reduced-Order Model (HPROM) technique, previously presented by the authors in the...
In recent years, there has been a growing interest in understanding complex microstructures and thei...
Proper Orthogonal Decomposition (POD) has been successfully used to reduce the size of linear Finite...
In the electrical engineering field, numerical simulation allows to avoid experiments which can be e...
Model Order Reduction (MOR) methods are used to cope with high computational costs typically involve...
Computational numerical methods are important tools in science and technology today. Numerical simul...
Numerical simulations currently represent one of the most efficient ways of studying complex physica...
The authors have shown in previous contributions that reduced order modeling with optimal cubature a...
In this work presents a strategy to diminish the computational cost of a hierarchical (FE2) multi-sc...
The authors have shown in previous contributions that reduced order modeling with optimal cubature a...
In this paper, we propose upper and lower error bounding techniques for reduced order modelling appl...