  In this paper, we develop a Wasserstein autoencoder (WAE) with a
hyperspherical prior for multimodal data in the application of inertial
confinement fusion. Unlike a typical hyperspherical generative model that
requires computationally inefficient sampling from distributions like the von
Mis Fisher, we sample from a normal distribution followed by a projection layer
before the generator. Finally, to determine the validity of the generated
samples, we exploit a known relationship between the modalities in the dataset
as a scientific constraint, and study different properties of the proposed
model.
