In recent years, research in deep learning techniques has attracted much attention. With the help of big data technology, massively parallel computing, and fast optimization algorithms, deep learning has dramatically improved the performance of many problems in speech and image research. In electromagnetic engineering, physical laws provide the theoretical foundation for research and development. With the development of deep learning, improving learning capacity may allow machines to “learn” from a large amount of physics data and “master” the physical law in certain controlled boundary conditions. In the long run, combining fundamental physical principles with “knowledge” from big data could unleash numerous engineering applications limited by a lack of data information and computation ability. In this short tutorial, the presenter will share some of his learnings in deep learning techniques and discuss the potential and feasibility of applying deep learning in computational electromagnetics. The presenter hopes to explore the characteristics, feasibility, and challenges of deep learning methods in the field of computational electromagnetics through some examples, such as solving wave equations, array antenna synthesis, inverse scattering, etc. Co-sponsored by: STARaCom Montreal Speaker(s): Prof. Maokun Li , Virtual: https://events.vtools.ieee.org/m/351680