Deep learning is increasingly being used to tackle a range of electromagnetic problems, such as developing fast modeling solvers, accurate imaging algorithms, efficient antenna design tools, and wireless link or channel characterization methods. This book highlights pioneering applications of deep learning in electromagnetic engineering, where Maxwell’s equations govern the underlying physics. As deep learning techniques advance, their improved learning and generalization capabilities may enable machines to learn from data and effectively master physical laws within certain boundaries. In the future, combining fundamental physical principles with data-driven knowledge could open up new possibilities in electromagnetic theory and engineering that were previously unattainable due to data and computational limitations.
The book covers deep learning applications in areas such as electromagnetic forward modeling, free-space inverse scattering, non-destructive testing and evaluation, subsurface and biomedical imaging, direction of arrival estimation, remote sensing, digital satellite communications, imaging and gesture recognition, metamaterials and metasurfaces design, and microwave circuit modeling.
Applications of Deep Learning in Electromagnetics is a valuable resource for researchers seeking new approaches to solving Maxwell’s equations, students of electromagnetic theory, and deep learning researchers interested in innovative applications.
