Insertion modeling and digital twin technology
DOI:
https://doi.org/10.15407/visn2025.11.064Keywords:
digital twins, insertion modeling, cybersecurity, formal verification, neural networks, machine learning, behavior algebra, neuro-symbolic approach, artificial intelligence.Abstract
The article provides an overview of the technology of insertion modeling and its use in the creation of digital twins. The main concepts of the theory of agents and environments, behavior algebra and the basics of symbolic multi-agent modeling are presented within the framework of the creation and functioning of digital twins in various areas, in particular, transportation systems, nuclear power industry and blockchain. The insertion modeling of continuous processes is considered, as well as a multi-level modeling method in medicine and biology. The neuro-insertion approach, which is based on a combination of artificial intelligence methods and insertion modeling, is considered. Examples of the application of the neuro-insertion approach in cybersecurity, as a model for predicting attacks with confirmation by the algebraic component of the digital twin, are presented.
Cite this article:
Letychevskyi O.O. Insertion modeling and digital twin technology. Visn. Nac. Akad. Nauk Ukr. 2025. (11): 64—77. https://doi.org/10.15407/visn2025.11.064
References
Letichevsky A.A., Kapitonova J.V. Algebraic Programming in the APS System. In: ISSAC'90: Proc. Int. symp. on symbolic and algebraic computation (20—25 August, 1990, Tokyo, Japan). P. 68—75. https://doi.org/10.1145/96877.9689
Kapitonova J.V., Letichevsky A.A., Konozenko S.V. Computations in APS. Theoretical Computer Science. 1993. 119(1): 145—171. https://doi.org/10.1016/0304-3975(93)90343-R
Letichevsky A.A., Gilbert D.R. Agents and environments. In: Proc. 1st Int. sci. conf. on programming (2—4 September, 1998, Kyiv, Glushkov Institute of Cybernetics of NAS of Ukraine). https://apsystems.org.ua/uploads/doc/ims/AE.eng.pdf
Letichevsky A. Algebra of behavior transformations and its applications. In: Kudryavtsev V.B., Rosenberg I.G., Goldstein M. (eds) Structural Theory of Automata, Semigroups, and Universal Algebra. NATO Science Series II: Mathematics, Physics and Chemistry. Vol. 207. Springer, Dordrecht, 2005. https://doi.org/10.1007/1-4020-3817-8_10
Milner R. A Calculus of Communicating Systems. Lecture Notes in Computer Science. Vol. 92. Springer, Berlin, 1980. https://doi.org/10.1007/3-540-10235-3
Hoare C. A. R. Communicating Sequential Processes. Englewood Cliffs, NJ: Prentice-Hall, 1985. https://doi.org/10.1145/359576.359585
Cardelli L., Gordon A.D. Mobile ambients. In: Nivat M. (ed.) Foundations of Software Science and Computation Structures. FoSSaCS 1998. Lecture Notes in Computer Science. Vol. 1378. Springer, Berlin, Heidelberg, 1998. P. 140—155. https://doi.org/10.1007/BFb0053547
Letichevsky A., Letychevskyi O., Peschanenko V., Weigert T. Insertion modeling and symbolic verification of large systems. In: Fischer J., Scheidgen M., Schieferdecker I., Reed R. (eds) SDL 2015: Model-Driven Engineering for Smart Cities. Lecture Notes in Computer Science. Vol. 9369. Springer, Cham, 2015. https://doi.org/10.1007/978-3-319-24912-4_1
Grieves M. Origins of the Digital Twin Concept. 2016. https://doi.org/10.13140/RG.2.2.26367.61609
Catmur J., King K., Parsons M., Tarada F. A Service Analysis of the Mont Blanc Tunnel Fire. In: Proc. Safety Critical Systems Symposium. York, UK, 2023.
Yang X., Yu S., Wang J., Chen H., Huang Y., Luo Z., Fu L. Application of a digital twin for highway tunnels based on multi-sensor and information fusion. Front. Phys. 2024. 12: 1335494. https://doi.org/10.3389/fphy.2024.1335494
Amit S., Kaushik R., Manas G. Neurosymbolic Artificial Intelligence (Why, What, and How). IEEE Intelligent Systems. 2023. 38 (3): 56—62. https://doi.org/10.1109/MIS.2023.3268724
Serafini L., d'Avila Garcez A. Logic Tensor Networks: Deep Learning and Logical Reasoning from Data and Knowledge. arXiv:1606.04422. https://doi.org/10.48550/arXiv.1606.04422
Giunchiglia E., Stoian M.C., Lukasiewicz T. Deep Learning with Logical Constraints. In: Proc. 31st Int. Joint Conf. on Artificial Intelligence. 2022. P. 5478—5485. https://doi.org/10.24963/ijcai.2022/767
Letichevsky A.A. Algebraic interaction theory and cyber-physical systems. Problems of Control and Informatics. 2017. 62(5): 37—55. https://doi.org/10.1615/JAutomatInfScien.v49.i9.10
Arnav B., Bernabeu-Perez P., Helm-Burger N., Kostolansky T., Whittingham H., Phuong M. CoT red-handed: Stress testing chain-of-thought monitoring. arXiv:2505.23575,2025. https://doi.org/10.48550/arXiv.2505.23575
Letychevskyi O., Peschanenko V. Applying algebraic virtual machine to cybersecurity tasks. In: Proc. 2022 IEEE 9th Int. Conf. on Sciences of Electronics, Technologies of Information and Telecommunications (SETIT), Hammamet, Tunisia, 2022. P. 161—169. https://doi.org/10.1109/SETIT54465.2022.9875895
Letychevskyi O.O., Tarasich Y.H. Neuro-symbolic approach for the biological systems and processes research. Problems in Programming. 2024. (2-3): 271—279. https://doi.org/10.15407/pp2024.02-03.271
Katsoulakis E., Wang Q., Wu H., Shahriyari L., Fletcher R., Liu J., Achenie L., Liu H., Jackson P., Xiao Y., Syeda-Mahmood T., Tuli R., Deng J. Digital twins for health: a scoping review. npj Digital Medicine. 2024. 7(1): 77. https://doi.org/10.1038/s41746-024-01073-0
