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

In this paper we propose local approximation spaces for localized model order reduction
procedures such as domain decomposition and multiscale methods. Those spaces are …

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 propose and rigorously analyze new variants of adaptive Trust-
Region methods for parameter optimization with PDE constraints and bilateral parameter …

We propose a probabilistic way for reducing the cost of classical projection-based model
order reduction methods for parameter-dependent linear equations. A reduced order model …

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 …

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 …

Abstract The Reduced Basis Method (RBM) is a rigorous model reduction approach for
solving parameterizedpartial differential equations. It identifies a low-dimensional subspace …

Engineers manually optimizing a structure using finite element based simulation software
often employ an iterative approach where in each iteration they change the structure slightly …

The objective of this thesis is to develop and analyze model order reduction approaches for
the efficient integration of parametrized mathematical models and experimental …