  Deep learning (DL) has achieved remarkable successes in many disciplines such
as computer vision and natural language processing due to the availability of
``big data''. However, such success cannot be easily replicated in many nuclear
engineering problems because of the limited amount of training data, especially
when the data comes from high-cost experiments. To overcome such a data
scarcity issue, this paper explores the applications of deep generative models
(DGMs) that have been widely used for image data generation to scientific data
augmentation. DGMs, such as generative adversarial networks (GANs), normalizing
flows (NFs), variational autoencoders (VAEs), and conditional VAEs (CVAEs), can
be trained to learn the underlying probabilistic distribution of the training
dataset. Once trained, they can be used to generate synthetic data that are
similar to the training data and significantly expand the dataset size. By
employing DGMs to augment TRACE simulated data of the steady-state void
fractions based on the NUPEC Boiling Water Reactor Full-size Fine-mesh Bundle
Test (BFBT) benchmark, this study demonstrates that VAEs, CVAEs, and GANs have
comparable generative performance with similar errors in the synthetic data,
with CVAEs achieving the smallest errors. The findings shows that DGMs have a
great potential to augment scientific data in nuclear engineering, which proves
effective for expanding the training dataset and enabling other DL models to be
trained more accurately.
