  Quantum scattering calculations for all but low-dimensional systems at low
energies must rely on approximations. All approximations introduce errors. The
impact of these errors is often difficult to assess because they depend on the
Hamiltonian parameters and the particular observable under study. Here, we
illustrate a general, system and approximation-independent, approach to improve
the accuracy of quantum dynamics approximations. The method is based on a
Bayesian machine learning (BML) algorithm that is trained by a small number of
rigorous results and a large number of approximate calculations, resulting in
ML models that accurately capture the dependence of the dynamics results on the
quantum dynamics parameters. Most importantly, the present work demonstrates
that the BML models can generalize quantum results to different dynamical
processes. Thus, a ML model trained by a combination of approximate and
rigorous results for a certain inelastic transition can make accurate
predictions for different transitions without rigorous calculations. This opens
the possibility of improving the accuracy of approximate calculations for
quantum transitions that are out of reach of rigorous scattering calculations.
