International audienceThis work considers the stability of Proper Orthogonal Decomposition (POD) basis interpolation on Grassmann manifolds for parametric Model Order Reduction (pMOR) in hyperelasticity. The article contribution is mainly about stability conditions, all defined from strong mathematical background. We show how the stability of interpolation can be lost if certain geometrical requirements are not satisfied by making a concrete elucidation of the local character of linearization. To this effect, we draw special attention to the Grassmannian Exponential map and optimal injectivity condition of this map, related to the cut--locus of Grassmann manifolds. From this, explicit stability conditions are established and can be directl...
When simulating mechanical systems the flexibility of the components often has to be taken into acco...
This report analyzes and validates possible applications of some model reduction methods for direct ...
This thesis proposes the use of Reduced Basis (RB) methods to improve the computational efficiency o...
International audienceThis work considers the stability of Proper Orthogonal Decomposition (POD) bas...
Parametric simulations of thermomechanical metal forming processes still remain computational costly...
International audienceThis paper deals with the extension of proper generalized decomposition method...
International audienceThis work aims to interpolate parametrized Reduced Order Model (ROM) basis con...
Proper Orthogonal Decomposition (POD) is an efficient model order reduction technique for linear pro...
Reduced-order models (ROM) are developed using the proper orthogonal decomposition (POD) for one dim...
The need for reduced order models (ROMs) has be- come considerable higher with the increasing techno...
This paper deals with the extension of Proper Generalized Decomposition (PGD) methods to non-linear ...
We present a technique for the approximation of a class of Hilbert space--valued maps which arise wi...
We provide an introduction to proper orthogonal decomposition (POD) model order reduction with focus...
Nowadays, the use of Hyper Reduced Order Models (HROMs) to tackle the high computational complexity ...
A novel model-order reduction technique for the solution of the fine-scale equilibrium problem appea...
When simulating mechanical systems the flexibility of the components often has to be taken into acco...
This report analyzes and validates possible applications of some model reduction methods for direct ...
This thesis proposes the use of Reduced Basis (RB) methods to improve the computational efficiency o...
International audienceThis work considers the stability of Proper Orthogonal Decomposition (POD) bas...
Parametric simulations of thermomechanical metal forming processes still remain computational costly...
International audienceThis paper deals with the extension of proper generalized decomposition method...
International audienceThis work aims to interpolate parametrized Reduced Order Model (ROM) basis con...
Proper Orthogonal Decomposition (POD) is an efficient model order reduction technique for linear pro...
Reduced-order models (ROM) are developed using the proper orthogonal decomposition (POD) for one dim...
The need for reduced order models (ROMs) has be- come considerable higher with the increasing techno...
This paper deals with the extension of Proper Generalized Decomposition (PGD) methods to non-linear ...
We present a technique for the approximation of a class of Hilbert space--valued maps which arise wi...
We provide an introduction to proper orthogonal decomposition (POD) model order reduction with focus...
Nowadays, the use of Hyper Reduced Order Models (HROMs) to tackle the high computational complexity ...
A novel model-order reduction technique for the solution of the fine-scale equilibrium problem appea...
When simulating mechanical systems the flexibility of the components often has to be taken into acco...
This report analyzes and validates possible applications of some model reduction methods for direct ...
This thesis proposes the use of Reduced Basis (RB) methods to improve the computational efficiency o...