Antenna Design Exploration and Optimization using Machine Learning

Design exploration using numerical field simulation is a valuable approach to analyze antenna performance parameters. In such a process many data describing a mapping from design variables to response functions are generated. In this work different machine learning (ML) techniques are applied on these data to analyze and optimize antenna performance. This data driven simulation approach can speed up antenna optimization tremendously. Also, the benefit of dimensionality reduction algorithms and evolutionary learning in antenna performance analysis is described.