  Conventional acoustic metasurfaces are constructed with gradiently ``local''
phase shift profiles provided by subunits. The local strategy implies the
ignorance of the mutual coupling between subunits, which limits the efficiency
of targeted sound manipulation, especially in complex environments. By taking
into account the ``nonlocal'' interaction among subunits, nonlocal metasurface
offers an opportunity for accurate control of sound propagation, but the
requirement of the consideration of gathering coupling among all subunits, not
just the nearest-neighbor coupling, greatly increases the complexity of the
system and therefore hinders the explorations of functionalities of nonlocal
metasurfaces. In this work, empowered by deep learning algorithms, the complex
gathering coupling can be learned efficiently from the preset dataset so that
the functionalities of nonlocal metasurfaces can be significantly uncovered. As
an example, we demonstrate that nonlocal metasurfaces, which can redirect an
incident wave into multi-channel reflections with arbitrary energy ratios, can
be accurately predicted by deep learning algorithms. Compared to the theory,
the relative error of the energy ratios is less than 1\%. Furthermore,
experiments witness three-channel reflection with three types of energy ratios
of (1, 0, 0), (1/2, 0, 1/2), and (1/3, 1/3, 1/3), proving the validity of the
deep learning enabled nonlocal metasurfaces. Our work might blaze a new trail
in the design of acoustic functional devices, especially for the cases
containing complex wave-matter interactions.
