  We present several high-accuracy surrogate models for gravitational-wave
signals from equal-mass head-on mergers of Proca stars, computed through the
Newman-Penrose scalar $\psi_4$. We also discuss the current state of the model
extensions to mergers of Proca stars with different masses, and the particular
challenges that these present. The models are divided in two main categories:
two-stage and monolithic. In the two-stage models, a dimensional reduction
algorithm is applied to embed the data in a reduced feature space, which is
then interpolated in terms of the physical parameters. For the monolithic
models, a single neural network is trained to predict the waveform from the
input physical parameter. Our model displays mismatches below $10^{-3}$ with
respect to the original numerical waveforms. Finally, we demonstrate the usage
of our model in full Bayesian parameter inference through the accurate recovery
of numerical relativity signals injected in zero-noise, together with the
analysis of GW190521. For the latter, we observe excellent agreement with
existing results that make use of full numerical relativity.
