Low-rank solvers for unsteady Stokes–Brinkman optimal control problem with random data
We consider the numerical simulation of an optimal control problem constrained by the
unsteady Stokes–Brinkman equation involving random data. More precisely, we treat the …
unsteady Stokes–Brinkman equation involving random data. More precisely, we treat the …
A rapid and efficient isogeometric design space exploration framework with application to structural mechanics
In this paper, we present an isogeometric analysis framework for design space exploration.
While the methodology is presented in the setting of structural mechanics, it is applicable to …
While the methodology is presented in the setting of structural mechanics, it is applicable to …
Block-diagonal preconditioning for optimal control problems constrained by PDEs with uncertain inputs
The goal of this paper is the efficient numerical simulation of optimization problems
governed by either steady-state or unsteady partial differential equations involving random …
governed by either steady-state or unsteady partial differential equations involving random …
A preconditioned low-rank projection method with a rank-reduction scheme for stochastic partial differential equations
In this study, we consider the numerical solution of large systems of linear equations
obtained from the stochastic Galerkin formulation of stochastic partial differential equations …
obtained from the stochastic Galerkin formulation of stochastic partial differential equations …
On the convergence of Krylov methods with low-rank truncations
Low-rank Krylov methods are one of the few options available in the literature to address the
numerical solution of large-scale general linear matrix equations. These routines amount to …
numerical solution of large-scale general linear matrix equations. These routines amount to …
Tensorial parametric model order reduction of nonlinear dynamical systems
For a nonlinear dynamical system that depends on parameters, this paper introduces a
novel tensorial reduced-order model (TROM). The reduced model is projection-based, and …
novel tensorial reduced-order model (TROM). The reduced model is projection-based, and …
Fast solution of three‐dimensional elliptic equations with randomly generated jum** coefficients by using tensor‐structured preconditioners
In this paper, we propose and analyze the numerical algorithms for fast solution of periodic
elliptic problems in random media in ℝ d R^ d, d= 2, 3 d= 2, 3. Both the two‐dimensional …
elliptic problems in random media in ℝ d R^ d, d= 2, 3 d= 2, 3. Both the two‐dimensional …
Interpolatory tensorial reduced order models for parametric dynamical systems
The paper introduces a reduced order model (ROM) for numerical integration of a dynamical
system which depends on multiple parameters. The ROM is a projection of the dynamical …
system which depends on multiple parameters. The ROM is a projection of the dynamical …
Low‐rank solution of an optimal control problem constrained by random Navier‐Stokes equations
We develop a low‐rank tensor decomposition algorithm for the numerical solution of a
distributed optimal control problem constrained by two‐dimensional time‐dependent Navier …
distributed optimal control problem constrained by two‐dimensional time‐dependent Navier …
A Low-rank solver for the Navier--Stokes equations with uncertain viscosity
We study an iterative low-rank approximation method for the solution of the steady-state
stochastic Navier--Stokes equations with uncertain viscosity. The method is based on …
stochastic Navier--Stokes equations with uncertain viscosity. The method is based on …