70 citations · 75 across the 5 of their papers we have counts for
6 papers
Stochastic Galerkin reduced basis methods for parametrized linear elliptic partial differential equations
Sebastian Ullmann, Christopher Müller, Jens Lang
We consider the estimation of parameter-dependent statistics of functional outputs of elliptic boundary value problems (BVPs) with parametrized random and deterministic inputs. For…
POD model order reduction with space-adapted snapshots for incompressible flows
Carmen Gräßle, Michael Hinze, Jens Lang +1
We consider model order reduction based on proper orthogonal decomposition (POD) for unsteady incompressible Navier-Stokes problems, assuming that the snapshots are given by spatia…
A Bramble-Pasciak conjugate gradient method for discrete Stokes equations with random viscosity
Christopher Müller, Sebastian Ullmann, Jens Lang
We study the iterative solution of linear systems of equations arising from stochastic Galerkin finite element discretizations of saddle point problems. We focus on the Stokes mode…
A weighted reduced basis method for parabolic PDEs with random data
Christopher Spannring, Sebastian Ullmann, Jens Lang
This work considers a weighted POD-greedy method to estimate statistical outputs parabolic PDE problems with parametrized random data. The key idea of weighted reduced basis method…
A Bramble-Pasciak conjugate gradient method for discrete Stokes problems with lognormal random viscosity
Christopher Müller, Sebastian Ullmann, Jens Lang
We study linear systems of equations arising from a stochastic Galerkin finite element discretization of saddle point problems with random data and its iterative solution. We consi…
POD-Galerkin reduced-order modeling with adaptive finite element snapshots
Sebastian Ullmann, Marko Rotkvic, Jens Lang
We consider model order reduction by proper orthogonal decomposition (POD) for parametrized partial differential equations, where the underlying snapshots are computed with adaptiv…