Developing machine learning and Deep Learning increases the opportunities in all 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 fails 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.
The former have been more extensively tested than the latter, for the time being.
``Physics-based'' is, however, wrong.
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.
Article A Review of Physics-based Machine Learning in Civil Engineering
https://www.frontiersin.org/articles/10.3389/frwa.2020.00008/full
https://www.quora.com/How-important-is-numerical-analysis-in-the-field-of-machine-learning
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