2 citations · 2 across the 2 of their papers we have counts for
5 papers
Localized model reduction for nonlinear elliptic partial differential equations: localized training, partition of unity, and adaptive enrichment
Kathrin Smetana, Tommaso Taddei
We propose a component-based (CB) parametric model order reduction (pMOR) formulation for parameterized {nonlinear} elliptic partial differential equations (PDEs). CB-pMOR is desig…
Optimal local approximation spaces for parabolic problems
Julia Schleuß, Kathrin Smetana
We propose local space-time approximation spaces for parabolic problems that are optimal in the sense of Kolmogorov and may be employed in multiscale and domain decomposition metho…
Stable and efficient Petrov-Galerkin methods for a kinetic Fokker-Planck equation
Julia Brunken, Kathrin Smetana
We propose a stable Petrov-Galerkin discretization of a kinetic Fokker-Planck equation constructed in such a way that uniform inf-sup stability can be inferred directly from the va…
Randomized residual-based error estimators for the Proper Generalized Decomposition approximation of parametrized problems
Kathrin Smetana, Olivier Zahm
This paper introduces a novel error estimator for the Proper Generalized Decomposition (PGD) approximation of parametrized equations. The estimator is intrinsically random: It buil…
Localized model reduction for parameterized problems
Andreas Buhr, Laura Iapichino, Mario Ohlberger +3
In this contribution we present a survey of concepts in localized model order reduction methods for parameterized partial differential equations. The key concept of localized model…