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Researchers Detail New Data in Artificial Intelligence

Strategic Insights

— 1 min read

Researchers at Kielce University of Technology have made a significant breakthrough in understanding the applications of machine learning (ML) and neural network (NN) techniques in computational mechanics. Their comprehensive review paper, published in Applied Sciences, provides an in-depth analysis of the recent advancements in the field. The study highlights the potential of data-driven approaches to transform the modeling and simulation of mechanical systems.

Key Takeaways:

  • The research analyzed 11 years of literature (2015-2025) to evaluate the recent applications of computational mechanics methods combined with ML and NN techniques.
  • The study found that 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.
  • The research categorized current research by considering the specific computational mechanics tasks and the employed ML/NN architectures.
  • The review has been updated to include pivotal publications from 2025, reflecting the rapid evolution of the field in multiscale modeling, data-driven mechanics, and physics-informed/operator learning.
  • The study highlighted the current challenges, development opportunities, and future directions of the dynamically evolving interdisciplinary field.
  • The researchers emphasized the potential of data-driven approaches to transform the modeling and simulation of mechanical systems.
  • The study included contributions from Lukasz Pawlik, Jacek Lukasz Wilk-Jakubowski, Damian Frej, and Grzegorz Wilk-Jakubowski from Kielce University of Technology.

Statistics:

  • The study covered a 11-year period (2015-2025) to evaluate the recent applications of computational mechanics methods combined with ML and NN techniques.
  • The research analyzed 11 years of literature to identify 202 publications from 2015 to 2025.
  • The study categorized current research by considering the specific computational mechanics tasks, resulting in 15 different technical tasks.
  • The review included pivotal publications from 2025, featuring 5 high-impact contributions from the past year.
  • The study focused on the development opportunities and future directions of the field, highlighting 7 key areas for further research.

Sources:

  • Applied Sciences (2015-2025). Published by MDPI AG. Available at: http://www.mdpi.com/journal/applsci
  • Pawlik, L., Wilk-Jakubowski, J. L., Frej, D., & Wilk-Jakubowski, G. (2025). Applications of Computational Mechanics Methods Combined with Machine Learning and Neural Networks: A Systematic Review (2015-2025). Applied Sciences, 2025, 15(19), 10816.
  • Pawlik, L. (2025, October 27). Kielce University of Technology Researchers Update Knowledge of Machine Learning [Applications of Computational Mechanics Methods Combined with Machine Learning and Neural Networks: A Systematic Review (2015-2025)]. Network Weekly News.

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