A Review of Physics-based Machine Learning in Civil Engineering

TL;DR

This paper reviews how physics-based machine learning integrates physical laws with data to improve civil engineering applications, addressing challenges like data mismatch and enhancing model reliability.

Contribution

It provides a comprehensive overview of the development and application of physics-based ML in civil engineering, highlighting its potential to solve real-world problems.

Findings

Physics-based ML helps mitigate data shift issues in civil engineering.

Integration of PDEs and data improves model robustness.

Physics-based ML is increasingly used in fluid dynamics and other fields.

Abstract

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.