Data are the core of deep learning (DL), and the quality of data
significantly affects the performance of DL models. However, high-quality and
well-annotated databases are hard or even impossible to acquire for use in many
applications, such as structural risk estimation and medical diagnosis, which
is an essential barrier that blocks the applications of DL in real life.
Physics-guided deep learning (PGDL) is a novel type of DL that can integrate
physics laws to train neural networks. It can be used for any systems that are
controlled or governed by physics laws, such as mechanics, finance and medical
applications. It has been shown that, with the additional information provided
by physics laws, PGDL achieves great accuracy and generalisation when facing
data scarcity. In this review, the details of PGDL are elucidated, and a
structured overview of PGDL with respect to data scarcity in various
applications is presented, including physics, engineering and medical
applications. Moreover, the limitations and opportunities for current PGDL in
terms of data scarcity are identified, and the future outlook for PGDL is
discussed in depth.