In this paper, we present an adaptive algorithm to construct response surface
approximations of high‐fidelity models using a hierarchy of lower fidelity models. Our …

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 …

The linear micro-instabilities driving turbulent transport in magnetized fusion plasmas (as
well as the respective nonlinear saturation mechanisms) are known to be sensitive with …

We formulate four novel context-aware algorithms based on model hierarchies aimed to
enable an efficient quantification of uncertainty in complex, computationally expensive …

Deterministic interpolation and quadrature methods are often unsuitable to address
Bayesian inverse problems depending on computationally expensive forward mathematical …

In this work, we propose a novel framework coupling state-of-the-art multi-fidelity Gaussian
Process modeling techniques with input-space war** for a cost-efficient construction of a …

In this work, we discuss measures to increase the scalability, robustness, and efficiency of
the Combination Technique. In particular, we introduce an asynchronous variant and …

We propose and analyze a general goal-oriented adaptive strategy for approximating
quantities of interest (QoIs) associated with solutions to linear elliptic partial differential …

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

A novel multi-level method for partial differential equations with uncertain parameters is
proposed. The principle behind the method is that the error between grid levels in multi-level …