"Most of the computational models used in engineering electromagnetics are deterministic in nature, which means that one deals with an exact set of input data in a sense of either material properties or geometry. However, there are problems with an uncertainty in the input data set as some system properties are partly or entirely unknown. Therefore, a stochastic approach is required to determine the relevant statistics about the given responses, thus providing the assessment of the related confidence intervals in the set of ...
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"Most of the computational models used in engineering electromagnetics are deterministic in nature, which means that one deals with an exact set of input data in a sense of either material properties or geometry. However, there are problems with an uncertainty in the input data set as some system properties are partly or entirely unknown. Therefore, a stochastic approach is required to determine the relevant statistics about the given responses, thus providing the assessment of the related confidence intervals in the set of numerical results obtained as an output of a given deterministic model. Of particular interest are definitely non-intrusive stochastic approaches that could be easily coupled with widely used well-established deterministic models, just by efficiently postprocessing numerical results arising from deterministic models."--
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