In many situations across computational science and engineering, multiple computational
models are available that describe a system of interest. These different models have varying …

This article provides an overview of multi-fidelity modeling trends. Fidelity in modeling refers
to the level of detail and accuracy provided by a predictive model or simulation. Generally …

Multifidelity surrogates are essential in cases where it is not affordable to have more than a
few high-fidelity samples, but it is affordable to have as many low-fidelity samples as …

Physical interactions between a fluid and structure, potentially manifested as self-sustained
or divergent oscillations, can be sensitive to many parameters whose values are uncertain …

It is important to design robust and reliable systems by accounting for uncertainty and
variability in the design process. However, performing optimization in this setting can be …

We consider the problem of generating a time-optimal quadrotor trajectory for highly
maneuverable vehicles, such as quadrotor aircraft. The problem is challenging because the …

We introduce an algorithm to locate contours of functions that are expensive to evaluate. The
problem of locating contours arises in many applications, including classification …

Most multi-fidelity schemes for optimization or reliability assessment rely on regression
surrogates, such as Gaussian processes. Contrary to these approaches, we propose a …

A methodology for shape optimization of aerodynamic bodies under uncertainties is
presented. Flow-related and geometrical uncertainties are considered and quantified by …

This paper develops mfEGRA, a multifidelity active learning method using data-driven
adaptively refined surrogates for failure boundary location in reliability analysis. This work …