  The spectral energy distribution of galaxies is a complex function of the
star formation history and geometrical arrangement of stars and gas in
galaxies. The computation of the radiative transfer of stellar radiation
through the dust distribution is time-consuming. This aspect becomes
unacceptable in particular when dealing with the predictions by semi-analytical
galaxy formation models populating cosmological volumes, to be then compared
with multi-wavelength surveys. Mainly for this aim, we have implemented an
artificial neural network algorithm into the spectro-photometric and radiative
transfer code GRASIL in order to compute the spectral energy distribution of
galaxies in a short computing time. This allows to avoid the adoption of
empirical templates that may have nothing to do with the mock galaxies output
by models. The ANN has been implemented to compute the dust emission spectrum
(the bottleneck of the computation), and separately for the star-forming
molecular clouds and the diffuse dust (due to their different properties and
dependencies). We have defined the input neurons effectively determining their
emission, which means this implementation has a general applicability and is
not linked to a particular galaxy formation model. We have trained the net for
the disc and spherical geometries, and tested its performance to reproduce the
SED of disc and starburst galaxies, as well as for a semi-analytical model for
spheroidal galaxies. We have checked that for this model both the SEDs and the
galaxy counts in the Herschel bands obtained with the ANN approximation are
almost superimposed to the same quantities obtained with the full GRASIL. We
conclude that this method appears robust and advantageous, and will present the
application to a more complex SAM in another paper.
