  Current gravitational wave (GW) detections rely on the existence of libraries
of theoretical waveforms. Consequently, finding new physics with GWs requires
libraries of non-standard models, which are computationally demanding. We
discuss how deep learning frameworks can be used to generate new waveforms
"learned" from a simulation dataset obtained, say, from numerical relativity
simulations. Concretely, we use the WaveGAN architecture of a generative
adversarial network (GAN). As a proof of concept we provide this neural network
(NN) with a sample of ($>500$) waveforms from the collisions of exotic compact
objects (Proca stars), obtained from numerical relativity simulations. Dividing
the sample into a training and a validation set, we show that after a
sufficiently large number of training epochs the NN can produce from 12\% to
25\% of the synthetic waveforms with an overlapping match of at least 95\% with
the ones from the validation set. We also demonstrate that a NN can be used to
predict the overlapping match score, with 90\% of accuracy, of new synthetic
samples. These are encouraging results for using GANs for data augmentation and
interpolation in the context of GWs, to cover the full parameter space of, say,
exotic compact binaries, without the need of intensive numerical relativity
simulations.
