Deep learning has started to be applied to solving many electromagnetic problems, including the development of fast modelling solvers, accurate imaging algorithms, efficient design tools for antennas, as well as tools for wireless links/channels characterization. The contents of this book represent pioneer applications of deep learning techniques to electromagnetic engineering, where physical principles described by the Maxwell's equations dominate. With the development of deep learning techniques, improvement in learning capacity and generalization ability may allow machines to "learn" from properly collected data and "master" the physical laws in certain controlled boundary conditions. In the long run, a hybridization of fundamental physical principles with knowledge from training data could unleash numerous possibilities in electromagnetic theory and engineering that used to be impossible due to the limit of data information and ability of computation.

• Chapter 1: An introduction to deep learning for electromagnetics
• Chapter 2: Deep learning techniques for electromagnetic forward modeling
• Chapter 3: Deep learning techniques for free-space inverse scattering
• Chapter 4: Deep learning techniques for non-destructive testing and evaluation
• Chapter 5: Deep learning techniques for subsurface imaging
• Chapter 6: Deep learning techniques for biomedical imaging
• Chapter 7: Deep learning techniques for direction of arrival estimation
• Chapter 8: Deep learning techniques for remote sensing
• Chapter 9: Deep learning techniques for digital satellite communications
• Chapter 10: Deep learning techniques for imaging and gesture recognition
• Chapter 11: Deep learning techniques for metamaterials and metasurfaces design
• Chapter 12: Deep learning techniques for microwave circuit modeling
• Chapter 13: Concluding remarks, open challenges, and future trends