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Investigating the application of deep learning for electromagnetic simulation prediction

Applications seeking to exploit electromagnetic scattering characteristics of an imaging or detection problem typically require a large number of electromagnetic simulations. Because these simulations are often computationally intensive, valuable resources are required to perform the simulations in an efficient and timely manner, which is not always freely available or accessible. In this work, we investigate the utility of deep learning for electromagnetic simulation prediction. Specifically, we explore using artificial neural networks to learn the connection between a generic object and its resulting bistatic radar cross section, thereby removing the need to repeatedly perform timely simulations. Such a system would be trained in an offline setting and consequently enable rapid bistatic radar cross section predictions for new objects in the future. While deep learning can be seen as a computationally expensive technique, this cost is only experienced during the training of the system and not subsequently in the acquisition of results. The goal of this work is to learn the applicability of deep learning for electromagnetic simulation prediction as well as its potential limitations. Several simple objects are investigated and a thorough statistical analysis will be used to assess the performance of our proposed method.