mlsanityData to 14 Oct 2026

A Review of Physics-based Machine Learning in Civil Engineering

arXiv 2110.04600 ↗First posted 9 Oct 2021cs.LG cs.AIcited by 9

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Abstract (arXiv)

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.

Key facts. Cited by 9 papers in the map (data through 14 Oct 2026). Builds on 28 of its 159 references that are in the map. First posted on arXiv 9 Oct 2021.

Builds on

28 of 159 references.

References · 28

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16 more
  1. 2018Deep Fluids: A Generative Network for Parameterized Fluid Simulationscited by 121
  2. 2019PPINN: Parareal Physics-Informed Neural Network for time-dependent PDEscited by 118
  3. 2019Predicting AC Optimal Power Flows: Combining Deep Learning and Lagrangian Dual Methodscited by 94
  4. 2018Deep UQ: Learning deep neural network surrogate models for high dimensional uncertainty quantificationcited by 81
  5. 2018Deep Learning of Vortex Induced Vibrationscited by 76
  6. 2019Physics-informed Autoencoders for Lyapunov-stable Fluid Flow Predictioncited by 67
  7. 2020Convolutional-network models to predict wall-bounded turbulence from wall quantitiescited by 47
  8. 2021Towards extraction of orthogonal and parsimonious non-linear modes from turbulent flowscited by 45
  9. 2020Machine learning for metal additive manufacturing: Predicting temperature and melt pool fluid dynamics using physics-informed neural networkscited by 42
  10. 2019Physics-guided Convolutional Neural Network (PhyCNN) for Data-driven Seismic Response Modelingcited by 40
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  16. 2021Natural Language Processing Accurately Categorizes Indications, Findings and Pathology Reports from Multicenter Colonoscopycited by 2

Built on by

Cited by year

Citing papers per year: 2021: 1; 2022: 4; 2023: 2; 2024: 1; 2025: 1.

9 papers.

  1. 2022Physics-Informed Machine Learning: A Survey on Problems, Methods and Applicationscited by 92
  2. 2023Physics-informed PointNet: On how many irregular geometries can it solve an inverse problem simultaneously? Application to linear elasticitycited by 7
  3. 2024A hybrid numerical methodology coupling Reduced Order Modeling and Graph Neural Networks for non-parametric geometries: applications to structural dynamics problemscited by 3
  4. 2025Physics-informed KAN PointNet: Deep learning for simultaneous solutions to inverse problems in incompressible flow on numerous irregular geometriescited by 3
  5. 2021Machine Learning-based Prediction of Porosity for Concrete Containing Supplementary Cementitious Materialscited by 0
  6. 2022Wastewater Pipe Condition Rating Model Using K- Nearest Neighborscited by 0
  7. 2022Wastewater Pipe Rating Model Using Natural Language Processingcited by 0
  8. 2022Use of BNNM for interference wave solutions of the gBS-like equation and comparison with PINNscited by 0
  9. 2023Estimating irregular water demands with physics-informed machine learning to inform leakage detectioncited by 0