Machine Learning (ML) algorithms typically require extensive training datasets in order to generate adequate predictions. However, many scientific applications suffer from the lack of extensive high-quality labeled data. Embedding physics-based constraints into training enables training on unlabeled data, reduces the minimum size of the labeled training datasets, resulting in high-quality generalizable models.
See Adv. Opt. Research publication and Github depository