  The integration of Reconfigurable Intelligent Surfaces (RISs) into wireless
environments endows channels with programmability, and is expected to play a
key role in future communication standards. To date, most RIS-related efforts
focus on quasi-free-space, where wireless channels are typically modeled
analytically. Many realistic communication scenarios occur, however, in
rich-scattering environments which, moreover, evolve dynamically. These
conditions present a tremendous challenge in identifying an RIS configuration
that optimizes the achievable communication rate. In this paper, we make a
first step toward tackling this challenge. Based on a simulator that is
faithful to the underlying wave physics, we train a deep neural network as
surrogate forward model to capture the stochastic dependence of wireless
channels on the RIS configuration under dynamic rich-scattering conditions.
Subsequently, we use this model in combination with a genetic algorithm to
identify RIS configurations optimizing the communication rate. We numerically
demonstrate the ability of the proposed approach to tune RISs to improve the
achievable rate in rich-scattering setups.
