  The recent development of machine learning (ML) and Deep Learning (DL)
increases the opportunities in all the sectors. ML is a significant tool that
can be applied across many disciplines, but its direct application to civil
engineering problems can be challenging. ML for civil engineering applications
that are simulated in the lab often fail in real-world tests. This is usually
attributed to a data mismatch between the data used to train and test the ML
model and the data it encounters in the real world, a phenomenon known as data
shift. However, a physics-based ML model integrates data, partial differential
equations (PDEs), and mathematical models to solve data shift problems.
Physics-based ML models are trained to solve supervised learning tasks while
respecting any given laws of physics described by general nonlinear equations.
Physics-based ML, which takes center stage across many science disciplines,
plays an important role in fluid dynamics, quantum mechanics, computational
resources, and data storage. This paper reviews the history of physics-based ML
and its application in civil engineering.
