  We present a single-layer feedforward artificial neural network architecture
trained through a supervised learning approach for the deconvolution of flow
variables from their coarse grained computations such as those encountered in
large eddy simulations. We stress that the deconvolution procedure proposed in
this investigation is blind, i.e. the deconvolved field is computed without any
pre-existing information about the filtering procedure or kernel. This may be
conceptually contrasted to the celebrated approximate deconvolution approaches
where a filter shape is predefined for an iterative deconvolution process. We
demonstrate that the proposed blind deconvolution network performs
exceptionally well in the a-priori testing of both two-dimensional Kraichnan
and three-dimensional Kolmogorov turbulence and shows promise in forming the
backbone of a physics-augmented data-driven closure for the Navier-Stokes
equations.
