  Reconfigurable intelligent surfaces (RIS) have recently received significant
attention as building blocks for smart radio environments and adaptable
wireless channels. By altering the space- and time-varying electromagnetic (EM)
properties, the RIS transforms the inherently stochastic nature of the wireless
environment into a programmable propagation channel. Conventionally, designing
RIS to yield the desired EM response requires trial-and-error by iteratively
investigating a large possibility of various geometries and materials through
thousands of full-wave EM simulations. In this context, deep learning (DL)
techniques are proving critical in reducing the computational cost and time of
RIS inverse design. Instead of explicitly solving Maxwell's equations, DL
models learn physics-based relationships through supervised training data.
Further, generative adversarial networks are shown to synthesize novel RIS
designs not previously seen in the literature. This article provides a synopsis
of DL techniques for inverse RIS design and optimization to yield targeted EM
response necessary for future wireless networks.
