Abstract
In previous themes we have addressed issues related to a probabilistic approach to addressing EMC problems. The need for such an approach is due to the inherent uncertainty of system parameters and configurations which make a deterministic approach difficult to justify. The implication of this is that full physical models of complete systems are hard to identify. In the current issue we examine how probabilistic approaches and the use of surrogate models, coupled with machine learning (ML) techniques, can help in making predictions of the behaviour of systems where uncertainties are built into models. ML is part of what is described as artificial intelligence (AI) and therefore likely to receive a lot of attention in future EMC studies.
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