  Deep learning (DL) relies heavily on data, and the quality of data influences
its performance significantly. However, obtaining high-quality, well-annotated
datasets can be challenging or even impossible in many real-world applications,
such as structural risk estimation and medical diagnosis. This presents a
significant barrier to the practical implementation of DL in these fields.
Physics-guided deep learning (PGDL) is a novel type of DL that can integrate
physics laws to train neural networks. This can be applied to any systems that
are controlled or governed by physics laws, such as mechanics, finance and
medical applications. It has been demonstrated that, with the additional
information provided by physics laws, PGDL achieves great accuracy and
generalisation in the presence of data scarcity. This review provides a
detailed examination of PGDL and offers a structured overview of its use in
addressing data scarcity across various fields, including physics, engineering
and medical applications. Moreover, the review identifies the current
limitations and opportunities for PGDL in relation to data scarcity and offers
a thorough discussion on the future prospects of PGDL.
