Application of Deep Learning in Near Field of EMI Scanning

In this paper, we present a brief summary of two implementations of deep learning in the field of near-field scanning technology for electromagnetic interference (EMI), specifically focusing on the near field to near field transformation and near field to far field transformation. Sufficient data is acquired through a randomly distributed dipoles array for deep learning training purposes. These two applications demonstrate significant potential in the EMI domain and offer innovative solutions for addressing EMI challenges.