Parametric model order reduction using reduced basis methods can be an effective tool for
obtaining quickly solvable reduced order models of parametrized partial differential equation …

In this work we review a reduced basis method for the solution of uncertainty quantification
problems. Based on the basic setting of an elliptic partial differential equation with random …

Reduced basis methods are projection-based model order reduction techniques for
reducing the computational complexity of solving parametrized partial differential equation …

We consider model order reduction by proper orthogonal decomposition (POD) for
parametrized partial differential equations, where the underlying snapshots are computed …

We present a new surrogate modeling technique for efficient approximation of input-output
maps governed by parametrized PDEs. The model is hierarchical as it is built on a full order …

In this contribution we consider localized, robust, and efficient a posteriori error estimation of
the localized reduced basis multiscale (LRBMS) method for parametric elliptic problems with …

The main focus of the present work is the inclusion of spatial adaptivity for the snapshot
computation in the offline phase of model order reduction utilizing proper orthogonal …

We present a projection-based model reduction formulation for parametrized time-
dependent nonlinear partial differential equations (PDEs). Our approach builds on the …

In this contribution, we are concerned with tight a posteriori error estimation for projection-
based model order reduction of inf \inf-sup \sup stable parameterized variational problems …

We present a general approach for the treatment of parameterized geometries in projection-
based model order reduction. During the offline stage, given (i) a family of parameterized …