  Integrated modelling of electron runaway requires computationally expensive
kinetic models that are self-consistently coupled to the evolution of the
background plasma parameters. The computational expense can be reduced by using
parameterized runaway generation rates rather than solving the full kinetic
problem. However, currently available generation rates neglect several
important effects; in particular, they are not valid in the presence of
partially ionized impurities. In this work, we construct a multilayer neural
network for the Dreicer runaway generation rate which is trained on data
obtained from kinetic simulations performed for a wide range of plasma
parameters and impurities. The neural network accurately reproduces the Dreicer
runaway generation rate obtained by the kinetic solver. By implementing it in a
fluid runaway electron modelling tool, we show that the improved generation
rates lead to significant differences in the self-consistent runaway dynamics
as compared to the results using the previously available formulas for the
runaway generation rate.
