In mechanical structures like airplanes, cars and houses, noise is generated and transmitted
through vibrations. To take measures to reduce this noise, vibrations need to be simulated …

This work is concerned with the kernel-based approximation of a complex-valued function
from data, where the response function of a partial differential equation in the frequency …

For the purpose of high-fidelity aircraft cabin noise simulations during early design phases,
we study three efficient solving approaches for the fully coupled finite element model of an …

In this paper, we present the parameter identification for inelastic and multi-scale problems.
First, the theoretical background of several fundamental methods used in the upscaling …

This contribution combines a low-rank matrix approximation through Singular Value
Decomposition (SVD) with second-order Krylov subspace-based Model Order Reduction …

Parametric model order reduction techniques have seen significant applications in the field
of multi-query problems like uncertainty quantification, optimization and sensitivity analysis …

We present an algorithm for constructing efficient surrogate frequency‐domain models of
(nonlinear) parametric dynamical systems in a non‐intrusive way. To capture the …

Predictive modeling involving simulation and sensor data at the same time, is a growing
challenge in computational science. Even with large-scale finite element models, a …

This contribution presents a combined framework to perform parametric surrogate modeling
of vibroacoustic problems that enables efficient training of large-scale problems. The …

This thesis addresses surrogate modeling and forward uncertainty propagation for
parametric/stochastic versions of Maxwell's source and eigenproblem. Surrogate modeling …