In this work we focus on reduced order modelling for problems for which the resulting reduced basis spaces show a slow decay of the Kolmogorov $n$-width, or, in practical calculations, its computational surrogate given by the magnitude of the eigenvalues returned by a proper orthogonal decomposition on the solution manifold. In particular, we employ an additional preprocessing during the offline phase of the reduced basis method, in order to obtain smaller reduced basis spaces. Such preprocessing is based on the composition of the snapshots with a transport map, that is a family of smooth and invertible mappings that map the physical domain of the problem into itself. Two test cases are considered: a fluid moving in a domain with deforming ...
Reduced-order models were derived for plane Couette flow using Galerkin projection, with orthonormal...
Doctor of PhilosophyDepartment of Mechanical and Nuclear EngineeringMingjun WeiProjection-based mode...
This study presents a collection of purely data-driven workflows for constructing reduced-order mode...
The aim of this work is to present a Model Order Reduction (MOR) procedure that is carried out by me...
The Kolmogorov $n$-width of the solution manifolds of transport-dominated problems can decay slowly....
peer reviewedThis article describes a bridge between POD-based model order reduction techniques and ...
A quadratic approximation manifold is presented for performing nonlinear, projection-based, model or...
We design a physics-aware auto-encoder to specifically reduce the dimensionality of solutions arisin...
International audienceWe review a few applications of reduced-order modeling in aeronautics and medi...
For over a century, reduced order models (ROMs) have been a fundamental discipline of theoretical fl...
International audienceKolmogorov n-widths and low-rank approximations are studied for families of el...
This work proposes an adaptive structure-preserving model order reduction method for finite-dimensio...
The aim of this work is to build a reduced order model for parametrized porous media equations. The ...
We present methodologies for reduced order modeling of convection dominated flows. Accordingly, thre...
A large variety of physical phenomena can be described by large-scale systems of linear ordinary dif...
Reduced-order models were derived for plane Couette flow using Galerkin projection, with orthonormal...
Doctor of PhilosophyDepartment of Mechanical and Nuclear EngineeringMingjun WeiProjection-based mode...
This study presents a collection of purely data-driven workflows for constructing reduced-order mode...
The aim of this work is to present a Model Order Reduction (MOR) procedure that is carried out by me...
The Kolmogorov $n$-width of the solution manifolds of transport-dominated problems can decay slowly....
peer reviewedThis article describes a bridge between POD-based model order reduction techniques and ...
A quadratic approximation manifold is presented for performing nonlinear, projection-based, model or...
We design a physics-aware auto-encoder to specifically reduce the dimensionality of solutions arisin...
International audienceWe review a few applications of reduced-order modeling in aeronautics and medi...
For over a century, reduced order models (ROMs) have been a fundamental discipline of theoretical fl...
International audienceKolmogorov n-widths and low-rank approximations are studied for families of el...
This work proposes an adaptive structure-preserving model order reduction method for finite-dimensio...
The aim of this work is to build a reduced order model for parametrized porous media equations. The ...
We present methodologies for reduced order modeling of convection dominated flows. Accordingly, thre...
A large variety of physical phenomena can be described by large-scale systems of linear ordinary dif...
Reduced-order models were derived for plane Couette flow using Galerkin projection, with orthonormal...
Doctor of PhilosophyDepartment of Mechanical and Nuclear EngineeringMingjun WeiProjection-based mode...
This study presents a collection of purely data-driven workflows for constructing reduced-order mode...