Abstract: Designing an electromagnetic device, as with many other devices, is an inverse problem. The issue is that the performance and some constraints on the inputs are provided but the solution to the design problem is non-unique. Additionally, conventionally, at the start of the design process, the information on potential solutions needs to be generated quickly so that a designer can make effective decisions before moving on to detailed performance analysis, but the amount of information that can be obtained from simple analysis tools is limited. Machine learning may be able to assist by increasing the amount of information available at the early stages of the design process. This is not a new concept, in fact it has been considered for several decades but has always been limited by the computational power available. Recent advances in machine learning might allow for the creation of a more effective “sizing” stage of the design process, thus reducing the cost of generating a final design. The goal of this paper is to review some of the work in applying artificial intelligence to the design and analysis of electromagnetic devices and to discuss what might be possible by considering some examples of the use of machine learning in several tools used in conventional design, which have been considered over the past three decades.
