Many parameter estimation problems arising in applications can be cast in the framework of
Bayesian inversion. This allows not only for an estimate of the parameters, but also for the …

In this work, we propose an efficient solution of the inverse Stefan problem by multi-fidelity
Bayesian optimization. We construct a multi-fidelity Gaussian process surrogate model by …

This article introduces an hp hp‐adaptive multi‐element stochastic collocation method,
which additionally allows to re‐use existing model evaluations during either hh‐or pp …

We propose and analyze a Stein variational reduced basis method (SVRB) to solve large-
scale PDE-constrained Bayesian inverse problems. To address the computational challenge …

Inverse problems and, in particular, inferring unknown or latent parameters from data are
ubiquitous in engineering simulations. A predominant viewpoint in identifying unknown …

This work is a follow-up to our previous contribution (“Convergence of sparse collocation for
functions of countably many Gaussian random variables (with application to elliptic PDEs)” …

We propose an efficient surrogate modeling technique for uncertainty quantification. The
method is based on a well-known dimension-adaptive collocation scheme. We improve the …

This work develops computationally efficient techniques for modeling and simulating
complex flows. It relies on the concept of adaptive numerical experimentation. Adaptive …