RAS MathematicsПрограммирование Programming and Computer Software

  • ISSN (Print) 0132-3474
  • ISSN (Online) 3034-5847

Constructing compartmental models of dynanic systems using a software package for symbolic computation in Julia

PII
10.31857/S0132347424020051-1
DOI
10.31857/S0132347424020051
Publication type
Article
Status
Published
Authors
Volume/ Edition
Volume / Issue number 2
Pages
33-44
Abstract
This paper considers the problem of constructing compartmental models of dynamic systems by using a software package for symbolic calculation written in Julia. The software package is aimed at unifying the formalized construction of compartmental models, taking into account the meaningful description of possible interactions among compartments and the influence of various factors on the evolution of systems. An approach to the development of the instrumental and methodological basis for modeling the dynamic systems the behavior of which can be described by one-step processes is developed. The proposed software package enables the symbolic representation of the differential equations of the model in both stochastic and deterministic cases. It is implemented in Julia and uses the Julia Symbolics computer algebra library. A comparison between the Julia Symbolics tools and some other computer algebra systems is carried out. The application of the developed software package to a compartmental model is considered. The results can be used to solve problems of constructing and studying dynamic models in natural sciences that are represented by onestep processes.
Keywords
компартментальные модели динамические системы компьютерная алгебра язык программирования Julia программный комплекс символьных вычислений
Date of publication
17.09.2025
Year of publication
2025
Number of purchasers
0
Views
16

References

  1. 1. Кулябов Д.С. Аналитический обзор систем символьных вычислений // Вестник РУДН. Сер. Математика. Информатика. Физика. 2007. № 1–2. С. 38–45.
  2. 2. Колесов Ю.Б., Сениченков Ю.Б. Компонентное моделирование сложных динамических систем. — СПб: Санкт-Петербургский политехнический университет Петра Великого, 2020.
  3. 3. Банщиков А.В., Бурлакова Л.А., Иртегов В.Д., Титоренко Т.Н. Символьные вычисления в моделировании и качественном анализе динамических систем // Вычислительные технологии. 2014. № 6. С. 3–18.
  4. 4. Banshchikov A., Vetrov A. Application of software tools for symbolic description and modeling of mechanical systems // CEUR Workshop Proceedings. 2. ser. ”ICCSDE 2020 – Proceedings of the 2nd International Workshop on Information, Computation, and Control Systems for Distributed Environments”. 2020. P. 33–42.
  5. 5. Демидова А.В., Дружинина О.В., Масина О.Н., Петров А.А. Разработка алгоритмического и программного обеспечения моделирования управляемых динамических систем с применением символьных вычислений и стохастических методов // Программирование. 2023. № 2. С. 54–68.
  6. 6. Кабанихин С.И., Криворотько О.И. Математическое моделирование эпидемии Уханьского коронавируса COVID-2019 и обратные задачи // Журнал вычислительной математики и математической физики. 2020. Т. 60. № 11. С. 1950–1961.
  7. 7. Hamelin F., Iggidr A., Rapaport A., Sallet G. Observability, Identifiability and Epidemiology A survey. 2023. Access mode: https://arxiv.org/abs/2011.12202.
  8. 8. Chebotaeva V., Vasquez P.A. Erlang-Distributed SEIR Epidemic Models with Cross- Diffusion // Mathematics. 2023. Vol. 11, no. 9. P. 2167. Access mode: https:// www.mdpi.com/2227-7390/11/9/2167.
  9. 9. Киселевская-Бабинина В.Я., Романюха А.А., Санникова Т.Е. Математическая модель течения Сovid-19 и прогноз тяжести инфекции // Математическое моделирование. 2023. Т. 35. № 5. С. 31–46.
  10. 10. Ghosh S., Volpert V., Banerjee M. An Epidemic Model with Time Delay Determined by the Disease Duration // Mathematics. 2022. Vol. 10, no. 15. P. 2561. Access mode: https://www.mdpi.com/2227-7390/ 10/15/2561.
  11. 11. Ariffin M., Gopal K., Krishnarajah I., Cheilias I., Adam M., Arasan J., Rahman N., Dom N., Sham N. Mathematical epidemiologic and simulation modelling of first wave COVID-19 in Malaysia // Scientific Reports. 2021. Vol. 11. P. 20739.
  12. 12. Roman H.E., Croccolo F. Spreading of Infections on Network Models: Percolation Clusters and Random Trees // Mathematics. 2021. Vol. 9, no. 23. P. 3054. Access mode: https://www.mdpi.com/2227-7390/9/23/3054.
  13. 13. Giordano G., Blanchini F., Bruno R., Colaneri P., Filippo A., Di Matteo A., Colaneri M. Modelling the COVID-19 epidemic and implementation of population-wide interventions in Italy // Nature Medicine. 2020. Vol. 26. P. 855–860.
  14. 14. Демидова А.В. Уравнения динамики популяций в форме стохастических дифференциальных уравнений // Вестник РУДН. Серия: Математика. Информатика. Физика. 2013. № 1. С. 67–76. Режим доступа: https://journals.rudn.ru/miph/ article/view/8319.
  15. 15. Gevorkyan M.N., Velieva T.R., Korolkova A.V., Kulyabov D.S., Sevastyanov L.A. Stochastic Runge–Kutta Software Package for Stochastic Differential Equations // Dependability Engineering and Complex Systems / ed. by Zamojski W., Mazurkiewicz J., Sugier J., Walkowiak T., and Kacprzyk J. — Cham : Springer International Publishing. — 2016. Vol. 470. P. 169–179.
  16. 16. Gevorkyan M.N., Demidova A.V., Korolkova A.V., Kulyabov D.S. Issues in the Software Implementation of Stochastic Numerical Runge–Kutta // Distributed Computer and Communication Networks / ed. by Vishnevskiy V. M. and Kozyrev D. V. Cham : Springer International Publishing. 2018. Vol. 919. P. 532–546.
  17. 17. Геворкян М.Н., Демидова А.В., Велиева Т.Р., Королькова А.В., Кулябов Д.С., Севастьянов Л.А. Реализация метода стохастизации одношаговых процессов в системе компьютерной алгебры // Программирование. 2018. № 2. С. 18–27.
  18. 18. Gardiner C.W. Handbook of Stochastic Methods: For Physics, Chemistry and the Natural Sciences. Heidelberg: Springer, 1985.
  19. 19. Van Kampen N. Stochastic Processes in Physics and Chemistry. Amsterdam: Elsevier, 1992.
  20. 20. Bezanson J., Karpinski S., Shah V., Edelman A. Julia: A Fast Dynamic Language for Technical Computing. 2012. Access mode: https://arxiv.org/abs/1209.5145.
  21. 21. Gowda S., Ma Y., Cheli A., Gwozzdz M., Shah V.B., Edelman A., Rackauckas C. High-Performance Symbolic-Numerics via Multiple Dispatch // ACM Commun. Comput. Algebra. 2022. jan. Vol. 55, no. 3. P. 92–96. Access mode: https: //doi.org/10.1145/3511528.3511535.
  22. 22. Кулябов Д.С., Королькова А.В. Компьютерная алгебра на Julia // Программирование. 2021. № 2. С. 44–50.
  23. 23. Fedorov A.V., Masolova A.O., Korolkova A.V., Kulyabov D.S. Application of a numerical-analytical approach in the process of modeling differential equations in the Julia language // Journal of Physics: Conference Series. 2020. dec. Vol. 1694, no. 1. P. 012026. Access mode: https://dx.doi. org/10.1088/1742-6596/1694/1/012026.
  24. 24. Abotaleb M.S., Makarovskikh T. Analysis of Neural Network and Statistical Models Used for Forecasting of a Disease Infection Cases // 2021 International Conference on Information Technology and Nanotechnology (ITNT). 2021. P. 1–7.
  25. 25. Tuluri F., Remata R., Walters W.L., Tchounwou P.B. Application of Machine Learning to Study the Association between Environmental Factors and COVID-19 Cases in Mississippi, USA // Mathematics. 2022. Vol. 10, no. 6. P. 850. Access mode: https://www.mdpi.com/2227-7390/ 10/6/850.
  26. 26. Roman H.E., Croccolo F. Spreading of Infections on Network Models: Percolation Clusters and Random Trees // Mathematics. 2021. Vol. 9, no. 23. P. 3054. Access mode: https://www.mdpi.com/2227-7390/ 9/23/3054.
  27. 27. Романюха А.А. Математические модели в иммунологии и эпидемиологии инфекционных заболеваний. Москва : Бином. Лаборатория знаний, 2011.
  28. 28. Kermack W.O., McKendrick A.G. Contributions to the mathematical theory of epidemics // Proceedings of the Royal Society of London. Series A, Containing Papers of a Mathematical and Physical Character. 1927. V. 115. P. 700–721.
  29. 29. Strauss R.R., Bishnu S., Petersen M.R. Comparing the Performance of Julia on CPUs versus GPUs and Julia-MPI versus Fortran-MPI: a case study with MPASOcean (Version 7.1) // EGUsphere. 2023. Vol. 2023. P. 1–22. Access mode: https://egusphere.copernicus. org/preprints/2023/egusphere-2023-57/.
  30. 30. Rackauckas C., Nie Q. DifferentialEquations. jl — A Performant and Feature-Rich Ecosystem for Solving Differential Equations in Julia // Journal of Open Research Software. 2017.
  31. 31. Loman T.E., Ma Y., Ilin V., Gowda S., Korsbo N., Yewale N., Rackauckas C., Isaacson S.A. Catalyst: Fast Biochemical Modeling with Julia. 2022. Access mode: https://www.biorxiv.org/content/early/2022/08/02/ 2022.07.30.502135.
  32. 32. Angevaare J., Feng Z., and Deardon R. Pathogen.jl: Infectious Disease Transmission Network Modeling with Julia // Journal of Statistical Software. 2022. Vol. 104, no. 4. P. 1–30. Access mode: https://www.jstatsoft.org/index.php/ jss/article/view/v104i04.
  33. 33. Апреутесей А.М.Ю., Королькова А.В., Кулябов Д.С. Возможности гибридного моделирования систем с управлением на языках Modelica и Julia // Распределенные компьютерные и телекоммуникационные сети: управление, вычисление, связь (DCCN–2020). Материалы XXIII Международной научной конференции. 2020. С. 433–440.
  34. 34. Apreutesey A.M.Y., Korolkova A.V., Kulyabov D.S. Hybrid modelling of the red algorithm in the Julia language // Journal of Physics: Conference Series. 7. Information Technology, Telecommunications and Control Systems, ITTCS 2020. 2020. P. 012025.
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