  Artificial neural networks have become important to improve the search for
admissible string compactifications and characterize them. In this paper we
construct the heterotic orbiencoder, a general deep autoencoder to study
heterotic orbifold models arising from various Abelian orbifold geometries. Our
neural network can be easily trained to successfully encode the large parameter
space of many orbifold geometries simultaneously, independently of the
statistical dissimilarities of their training features. In particular, we show
that our autoencoder is capable of compressing with good accuracy the large
parameter space of two promising orbifold geometries in just three parameters.
Further, most orbifold models with phenomenologically appealing features appear
in bounded regions of this small space. Our contribution hints towards a
possible simplification of the classification of (promising) heterotic orbifold
models.
