  We present a preliminary strategy for modeling multidimensional distributions
through neural networks. We study the efficiency of the proposed strategy by
considering as input data the two-dimensional next-to-next leading order (NNLO)
jet k-factors distribution for the ATLAS 7 TeV 2011 data. We then validate the
neural network model in terms of interpolation and prediction quality by
comparing its results to alternative models.
