Innovations
Vol. 14 (2026), Issue 2, pg(s) 53-58

INNOVATION POLICY AND INNOVATION MANAGEMENT

Application of Artificial Intelligence in Mechanical Engineering Mechanics and Fracture Mechanics with an Overview of ANSYS SimAI Platform

  • 1 University of Slavonski Brod, Croatia
  • 2 Industrial and Trade School Slavonski Brod, Croatia

Abstract

Development of artificial intelligence in recent years has opened new possibilities in numerical analysis and engineering design. This paper systemizes the main area of applications of artificial intelligence in engineering with emphasis on computational mechanics. Fundamental approaches of machine learning are described including artificial neural networks, convolutional neural networks (CNN) and physics-informed neural networks and their integration with the finite element method. Additionally, a review is given on the platform ANSYS SimAI platform which combines predictive accuracy of numerical simulations with the speed of generative artificial intelligence in a cloud or local computer environment reducing computational time by one or two orders of magnitude. It is worth noting that artificial intelligence is not a replacement for classical analysis, but a powerful tool which speeds up the innovation process and can expand boundaries of numerical modeling in engineering

Keywords

References

  1. A. Ani, R. Nakka, G. Subhash, J.-F. Molinari, S. A. Ponnusami, Machine learning for computational fracture and damage mechanics - Status and perspectives, Engineering Fracture Mechanics, vol. 332, 111778, 2026.
  2. S. H. Moghtaderi, P. Thamburaja, M. A. M. Jedi, M. Beer, S. Abdullah, A. K. Ariffin, Recent advances of machine learning in fracture mechanics of quasi-brittle materials: A review, Jurnal Kejuruteraan, vol. 37, no. 7, pp. 3151–3172, 2025.
  3. L. Niu, N. Li, J. Guan, C. Xie, L. Li, S. Chen, Machine learning-based prediction approaches for the fracture toughness of quasi-brittle materials, Engineering Fracture Mechanics, vol. 327, 111449, 2025.
  4. F. Aldakheel, E. S. Elsayed, Y. Heider, and O. Weeger, From PINNs to PIKANs: Recent advances in physics-informed machine learning, Mach. Learn. Comput. Sci. Eng., vol. 1, p. 18, 2025, doi: 10.1007/s44379-025-00019-x.
  5. S. Cuomo, V. S. Di Cola, F. Giampaolo, G. Rozza, M. Raissi, and F. Piccialli, Scientific machine learning through physics-informed neural networks: Where we are and what's next, J. Sci. Comput., vol. 92, p. 88, 2022, doi: 10.1007/s10915-022-01939-z.
  6. M. Rouhani, A. Dolatshahi, New models for estimating pure shear fracture toughness of confined quasi-brittle PTS specimens: Empirical and machine learning framework, Results in Engineering, 28, 2025., 107418., doi: https://doi.org/10.1016/j.rineng.2025.107418
  7. S. K. Sharma and P. Kumar, Artificial intelligence and machine learning in mechanical engineering: A comprehensive review, Engineering Applications of Artificial Intelligence, vol. 135, 108907, 2025.
  8. S. Mondal and S. S. Goswami, Rise of intelligent machines: Influence of artificial intelligence on mechanical engineering innovation, Spectrum of Engineering and Management Sciences, vol. 2, no. 1, pp. 46–55, 2024, doi: 10.31181/sems1120244h.
  9. T. Kreuzer, P. Papapetrou, J. Zdravković, Artificial intelligence in digital twins - A systematic literature review, Journal of Manufacturing Systems, vol. 78, pp. 142–162, 2024.
  10. C. Rao, H. Sun, Y. Liu, Physics-informed deep learning for computational elastodynamics without labeled data. Journal of Engineering Mechanics, 147 (8), 04021043, 2021. https://doi.org/10.1061/(ASCE)EM.1943-7889.0001947
  11. X. Guo, W. Li, F. Iorio, Convolutional neural networks for steady flow approximation. Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, 481–490. 2021. https://doi.org/10.1145/2939672.2939738
  12. D. W. Abueidda, S. Koric, N, A. Sobh, H. Schitoglu, Deep learning for plasticity and thermo-viscoplasticity. International Journal of Plasticity, 136, 102852, 2022. https://doi.org/10.1016/j.ijplas.2020.102852
  13. B. Zheng, T. Li, H. Qi, L. Gao, X. Liu, L. Yuan, Physics-informed machine learning model for computational fracture of quasi-brittle materials without labelled data. International Journal of Mechanical Sciences, 223, 107282, 2023. https://doi.org/10.1016/j.ijmecsci.2022.107282
  14. L. Liang, M. Liu, C. Martin, W. Sun, A deep learning approach to estimate stress distribution: a fast and accurate surrogate of finite-element analysis. Journal of the Royal Society Interface, 15 (138), 20170844, 2022. https://doi.org/10.1098/rsif.2017.0844
  15. S. Liao, G. Gu, L. Qin, Y. Chen, Review Physics-informed Neural Networks (PINN) for computational solid mechanics: Numerical frameworks and applications, Thin-Walled Structures, vol. 206, 111495, 2024. doi: 10.1016/j.tws.2024.112495
  16. J. Wang, A. Krishnamurthy, B. Starly, Deep learning-based finite element analysis (FEA) surrogate for sub-sea pressure vessel, arXiv preprint, arXiv:2206.03322, 2022.
  17. https://doi.org/10.48550/arXiv.2206.03322
  18. T.‑T. Le, V.‑H. Nguyen, M.‑V. Le, Development of deep learning model for the recognition of cracks on concrete surfaces, Advances in Civil Engineering, 8858545, 2021. https://doi.org/10.1155/2021/8858545
  19. W. Qayum, R. Ethisham, A. Bahrami, J. Mir, Q. U. Z. Khan, A. Ahmad, Y. O. Ozkilic, Predicting characteristics of cracks in concrete structure using convolutional neural network and image processing, Frontiers in Materials, vol. 10, 1210543, 2023. https://doi.org/10.3389/fmats.2023.1210543
  20. Y. Zhang, Q. Wang, Physics-Informed neural network solver for numerical analysis in mechanics and engineering, International Journal of Structural Integrity, vol. 15, no. 1, 1–25, 2024. https://doi.org/10.1080/17499518.2024.2315301
  21. S. Cai, Z. Wang, F. Lu, M. A. Buttacavoli, G. E. Karniadakis, Physics‑informed neural networks for heat transfer problems, Journal of Heat Transfer Journal of Heat Transfer, vol. 143, no. 6, p. 060801, 2021.
  22. Ansys Inc., Ansys Launches Ansys SimAI™ – New ultra‑fast AI‑based addition to the Ansys portfolio, Press Release, Pittsburgh, 2024.
  23. Ansys Inc., Explaining SimAI: How AI is Applied to Numerical Simulation, Ansys Blog, 2024.
  24. B. D’Amore, ML‑Enhanced Computer‑Aided Engineering in the Cloud, Machine Design, 2025.
  25. Develop3D, Ansys launches SimAI to enable more virtual testing, Develop3D Magazine, 2024.

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