  Scientific research's mandate is to comprehend and explore the world, as well
as to improve it based on experience and knowledge. Knowledge embedding and
knowledge discovery are two significant methods of integrating knowledge and
data. Through knowledge embedding, the barriers between knowledge and data can
be eliminated, and machine learning models with physical common sense can be
established. Meanwhile, humans' understanding of the world is always limited,
and knowledge discovery takes advantage of machine learning to extract new
knowledge from observations. Knowledge discovery can not only assist
researchers to better grasp the nature of physics, but it can also support them
in conducting knowledge embedding research. A closed loop of knowledge
generation and usage are formed by combining knowledge embedding with knowledge
discovery, which can improve the robustness and accuracy of models and uncover
previously unknown scientific principles. This study summarizes and analyzes
extant literature, as well as identifies research gaps and future
opportunities.
