  The ability to generate physically plausible ensembles of variable sources is
critical to the optimization of time-domain survey cadences and the training of
classification models on datasets with few to no labels. Traditional data
augmentation techniques expand training sets by reenvisioning observed
exemplars, seeking to simulate observations of specific training sources under
different (exogenous) conditions. Unlike fully theory-driven models, these
approaches do not typically allow principled interpolation nor extrapolation.
Moreover, the principal drawback of theory-driven models lies in the
prohibitive computational cost of simulating source observables from {\it ab
initio} parameters. In this work, we propose a computationally tractable
machine learning approach to generate realistic light curves of periodic
variables capable of integrating physical parameters and variability classes as
inputs. Our deep generative model, inspired by the Transparent Latent Space
Generative Adversarial Networks (TL-GANs), uses a Variational Autoencoder (VAE)
architecture with Temporal Convolutional Network (TCN) layers, trained using
the \hbox{OGLE-III} optical light curves and physical characteristics (e.g.,
effective temperature and absolute magnitude) from Gaia DR2. A test using the
temperature-shape relationship of RR\,Lyrae demonstrates the efficacy of our
generative "Physics-Enhanced Latent Space VAE" (PELS-VAE) model. Such deep
generative models, serving as non-linear non-parametric emulators, present a
novel tool for astronomers to create synthetic time series over arbitrary
cadences.
