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 …

We present a numerical method for utilizing stochastic models with differing fidelities to
approximate parameterized functions. A representative case is where a high-fidelity and a …

The paper reviews the application of deterministic‐stochastic models in some areas of
computational electromagnetics. Namely, in certain problems there is an uncertainty in the …

We present an application of adjoint analysis for efficient sensitivity analysis and estimation
of quantities of interest in the presence of uncertain model parameters in 3-D finite element …

Models in electromagnetism are more and more accurate. In some applications, the gap
between the experience and the model comes from the deviation on input data of the model …

We propose a new technique based on parametric model order reduction to efficiently
calculate the polynomial chaos expansion of the induced electric field in the human brain in …

We present the implementation and use of the Kriging methodology, ie, surrogate models
based on Kriging interpolation, in uncertainty quantification (UQ) in computational …

Model order reduction methods enable reduction of the computation time when dealing with
parametrized numerical models. Among these methods, the proper orthogonal …

We summarize several challenges in uncertainty quantification involving simulations of
electromagnetic scattering. While the finite element method (FEM) provides an excellent …