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Applications of Computational Mechanics Methods Combined with Machine Learning and Neural Networks: A Systematic Review (2015–2025)

Lukasz Pawlik, Jacek Lukasz Wilk-Jakubowski

2025Englishcomputational mechanicsmachine learningneural networkssurrogate modelingmultiscale modelinguncertainty quantification

Abstract

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This review paper analyzes the recent applications of computational mechanics methods in combination with machine learning (ML) and neural network (NN) techniques, as found in the literature published between 2015 and 2024. We present how ML and NNs are enhancing traditional computational methods, such as the finite element method, enabling the solution of complex problems in material modeling, surrogate modeling, inverse analysis, and uncertainty quantification. We categorize current research by considering the specific computational mechanics tasks and the employed ML/NN architectures. Furthermore, we discuss the current challenges, development opportunities, and future directions of this dynamically evolving interdisciplinary field, highlighting the potential of data-driven approaches to transform the modeling and simulation of mechanical systems. The review has been updated to include pivotal publications from 2025, reflecting the rapid evolution of the field in multiscale modeling, data-driven mechanics.

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Cite This Work

@article{99de0fd2-d904-4f7f-bc70-f5ca03711df3,
  title={Applications of Computational Mechanics Methods Combined with Machine Learning and Neural Networks: A Systematic Review (2015–2025)},
  author={Lukasz Pawlik and Jacek Lukasz Wilk-Jakubowski},
  year={2025},
  language={English}
}
TY  - JOUR
TI  - Applications of Computational Mechanics Methods Combined with Machine Learning and Neural Networks: A Systematic Review (2015–2025)
AU  - Lukasz Pawlik
AU  - Jacek Lukasz Wilk-Jakubowski
PY  - 2025
LA  - English
ER  -

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