  We propose a metasurface antenna capable of real time holographic beam
steering. An array of reconfigurable dipoeles can generate on demand far field
patterns of radiation through the specific encoding of meta atomic states.
i.e., the configuration of each dipole. Suitable states for the generation of
the desired patterns can be identified using iteartion, but this is very slow
and needs to be done for each far field pattern. Here, we present a deep
learning based method for the control of a metasurface antenna with point
dipole elements that vary in their state using dipole polarizability. Instead
of iteration, we adopt a deep learning algorithm that combines an autoencoder
with an electromagnetic scattering equation to determin the states required for
a target far field pattern in real time. The scattering equation from Born
approximation is used as the decoder in training the neural network, and
analytic Green's function calculation is used to check the validity of Born
approximation. Our learning based algorithm requires a computing time of within
in 200 microseconds to determine the meta atomic states, thus enabling the real
time opeartion of a holographic antenna.
