In the presence of nonaffine and highly nonlinear terms in parametrized partial differential equations, the standard Galerkin reduced-order approach is no longer efficient, because the evaluation of these terms involves high computational complexity. An efficient reduced-order approach is developed to deal with “nonaffineness” and nonlinearity. The efficiency and accuracy of the approach are demonstrated on several test cases, which show significant computational savings relative to classical numerical methods and relative to the standard Galerkin reduced-order approach.Singapore-MIT Alliance (SMA
Thesis (S.M.)--Massachusetts Institute of Technology, Computation for Design and Optimization Progra...
Reduction strategies, such as model order reduction (MOR) or reduced basis (RB) methods, in scientif...
Reduced order modeling has gained considerable attention in recent decades owing to the advantages ...
In this paper, we extend the reduced-basis approximations developed earlier for linear elliptic and ...
In this paper, we extend the reduced-basis approximations developed earlier for linear elliptic and ...
The nonlinear Galerkin methods have been proposed as improvements over the standard Galerkin methods...
Abstract We present a model order reduction technique for parametrized nonlinear reaction-diffusion ...
Abstract. In this paper, we extend the reduced-basis approximations developed earlier for linear ell...
This thesis is concerned with the development, analysis and implementation of efficient reduced orde...
This work has been motivated by optimizing the efficiency of a ship-propulsion device, the Voith-Sch...
Reduction strategies, such as model order reduction (MOR) or reduced basis (RB) methods, in scientic...
A novel parameterized non-intrusive reduced order model (P-NIROM) based on proper orthogonal decompo...
Developing reduced-order models for nonlinear parabolic partial differential equation (PDE) systems ...
We apply the reduced basis method to solve Navier-Stokes equations in parametrized domains. Special ...
We propose a computationally efficient framework to treat nonlinear partial differential equations h...
Thesis (S.M.)--Massachusetts Institute of Technology, Computation for Design and Optimization Progra...
Reduction strategies, such as model order reduction (MOR) or reduced basis (RB) methods, in scientif...
Reduced order modeling has gained considerable attention in recent decades owing to the advantages ...
In this paper, we extend the reduced-basis approximations developed earlier for linear elliptic and ...
In this paper, we extend the reduced-basis approximations developed earlier for linear elliptic and ...
The nonlinear Galerkin methods have been proposed as improvements over the standard Galerkin methods...
Abstract We present a model order reduction technique for parametrized nonlinear reaction-diffusion ...
Abstract. In this paper, we extend the reduced-basis approximations developed earlier for linear ell...
This thesis is concerned with the development, analysis and implementation of efficient reduced orde...
This work has been motivated by optimizing the efficiency of a ship-propulsion device, the Voith-Sch...
Reduction strategies, such as model order reduction (MOR) or reduced basis (RB) methods, in scientic...
A novel parameterized non-intrusive reduced order model (P-NIROM) based on proper orthogonal decompo...
Developing reduced-order models for nonlinear parabolic partial differential equation (PDE) systems ...
We apply the reduced basis method to solve Navier-Stokes equations in parametrized domains. Special ...
We propose a computationally efficient framework to treat nonlinear partial differential equations h...
Thesis (S.M.)--Massachusetts Institute of Technology, Computation for Design and Optimization Progra...
Reduction strategies, such as model order reduction (MOR) or reduced basis (RB) methods, in scientif...
Reduced order modeling has gained considerable attention in recent decades owing to the advantages ...
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