  We have generated a parametrization of the Compton form factor (CFF) H based
on data from deeply virtual Compton scattering (DVCS) using neural networks.
This approach offers an essentially model-independent fitting procedure, which
provides realistic uncertainties. Furthermore, it facilitates propagation of
uncertainties from experimental data to CFFs. We assumed dominance of the CFF H
and used HERMES data on DVCS off unpolarized protons. We predict the beam
charge-spin asymmetry for a proton at the kinematics of the COMPASS II
experiment.
