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In this article, we demonstrate how a generative neural network enables the inverse design of a real-time beamforming structure that consists of simply eight rotatable dielectric slabs but involves high nonlinearity. The determination of the optimal architecture and the detailed training process is discussed, and the advantage of the proposed method over some conventional numerical methods is shown. The system is realized and measured with a platform controlled by a low-cost system-on-a-chip (SoC) computer to verify its real-time design capability. We showcase the proposed neural network as a competitive candidate for complex real-time reconfigurable EM designs. [ABSTRACT FROM AUTHOR]