The tremendous development of digital computers in the last decades has provided continuous improvement of various sophisticated methods in computational electromagnetics (CEM). CEM analysis methods provide results within an appreciably shorter time than it would be required to build and test the corresponding prototype by means of certain experimental procedures. Furthermore, computer simulation of electromagnetic phenomena enables one to predict a system behavior for a wide range of parameters such as geometry, material properties, boundary and initial conditions, and excitations. Limitations of CEM models when applied to complex configurations resulting in large-scale problems are often related to very high computational cost.

On the other hand, significant discrepancies between calculated and measured results mainly stem due to uncertainties in an input data set arising from numerical instabilities, geometry, manufacturing defects, noise in measurements, and unpredictable and uncontrollable environmental effects. Therefore, of crucial interest for CEM models is to quantify uncertainty propagation due to various sources of uncertainty, as well as to obtain a meaningful statistics of the target variables by resorting to a limited number of simulations.

A plausible and efficient measure of these uncertainties in a set of inputs is one of stochastic methods. Namely, stochastic or stochastic-deterministic modeling can be applied with the objective to quantify such uncertainties by providing one or more output parameters, as random variables, and by computing corresponding statistical moments. In particular, stochastic problems could be divided into two groups: the first one dealing with deterministic system and stochastic fields and the second one when the parameters of the system are unknown.

This special issue aims to invite prospective authors to submit full-length journal papers covering all aspects of stochastic computational electromagnetics. All submitted manuscripts will be subjected to the peer-review process.

Potential topics include but are not limited to the following:

• Uncertainty quantification
• Uncertainty propagation, reliability analysis, and sensitivity analysis
• Stochastic collocation
• Stochastic finite element methods (SFEMs)
• Stochastic reduced order models (SROMs)
• Polynomial chaos expansion