Algorithms of Numerical-spectral Methods for Modeling Stochastic Dynamical Systems

28

Abstract

Representations of iterated Stratonovich and Ito stochastic integrals are obtained on the basis of the spectral form of mathematical description, they have both theoretical and practical significance. The latter is due to the possibility of constructing quite simple algorithms for the approximate modeling iterated stochastic integrals, which are necessary for the implementation of numerical methods for solving stochastic differential equations. The use of spectral representations of iterated stochastic integrals in numerical methods forms the numerical-spectral methods. Algorithms for them are presented in the form of programs for the computer algebra system Mathcad.

General Information

Keywords: iterated stochastic integrals, spectral form of mathematical description, spectral meth-od, stochastic differential equation, numerical-spectral method, numerical method

Journal rubric: Optimization Methods

Article type: scientific article

DOI: https://doi.org/10.17759/mda.2023130306

Received: 28.07.2023

Accepted:

For citation: Rybakov K.A. Algorithms of Numerical-spectral Methods for Modeling Stochastic Dynamical Systems. Modelirovanie i analiz dannikh = Modelling and Data Analysis, 2023. Vol. 13, no. 3, pp. 79–95. DOI: 10.17759/mda.2023130306. (In Russ., аbstr. in Engl.)

References

  1. Averina T.A. Statisticheskoe modelirovanie reshenii stokhasticheskikh differentcialnykh uravnenii i sistem so sluchainoi strukturoi [Statistical Modeling of Solutions of Stochastic Differential Equations and Systems with a Random Structure]. Novosibirsk, Siberian Branch of the Russian Academy of Sciences Publ., 2019. (In Russ.).
  2. Gikhman I.I., Skorokhod A.V. Introduction to the Theory of Random Processes. Dover Publ., 1997.
  3. Kuznetsov D.F. Stokhasticheskie differentcialnye uravneniia: teoriia i praktika chislennogo resheniia. S programmami v srede MATLAB [Stochastic differential equations: Theory and practice of numerical solution. With programs on MATLAB]. Differencialnie Uravnenia i Protsesy Upravlenia = Differential Equations and Control Processes, 2018, no. 4, pp. A.1–A.1073. (In Russ.).
  4. Levakov A.A. Stokhasticheskie differentcialnye uravneniia [Stochastic Differential Equations]. Minsk, Belarusian State University Publ., 2009. (In Russ.).
  5. Rybakov K.A. Spektralnyi metod modelirovaniia lineinykh nepreryvnykh stokhasticheskikh system [Spectral Method for Modeling Linear Continuous Stochastic Systems]. Moscow, Moscow Aviation Institute Publ., 2021. (In Russ.).
  6. Rybakov K.A. Spektralnoe predstavlenie povtornykh stokhasticheskikh integralov [Spectral representation of iterated stochastic integrals]. Otkrytye evoliutcioniruiushchie sistemy: Tcifrovaia transformatciia = Open Evolving Systems: Digital Transformation. Proceedings of Sixth International Scientific and Practical Conference, Khabarovsk, June 8–9, 2022. Khabarovsk, Far-Eastern State Transport University Publ., 2022, pp. 145–162. (In Russ.).
  7. Rybakov K.A. Osobennosti razlozheniia kratnykh stokhasticheskikh integralov Stratonovicha s primeneniem funktcii Uolsha i Khaara [Features of the expansion of multiple stochastic Stratonovich integrals using Walsh and Haar functions]. Differencialnie Uravnenia i Protsesy Upravlenia = Differential Equations and Control Processes, 2023, no. 1, pp. 137–150. (In Russ.).
  8. Rybakov K.A. Exact calculation of the approximation error of multiple Ito stochastic integrals. Numerical Analysis and Applications, 2023, vol. 26, no. 2, pp. 205–213.
  9. Rybin V.V. Modelirovanie nestatcionarnykh nepreryvno-diskretnykh sistem upravleniia spektralnym metodom v sistemakh kompiuternoi matematiki [Modeling of Nonstationary Continuous-Discrete Control Systems by Spectral Method on Computers]. Moscow, Moscow Aviation Institute Publ., 2011. (In Russ.).
  10. Solodovnikov V.V., Semenov V.V., Peshel M., Nedo D. Raschet sistem upravleniia na TcVM: spektralnyi i interpoliatcionnyi metody [Design of Control Systems on Digital Computers: Spectral and Interpolational Methods]. Moscow, Mashinostroenie Publ., 1979. (In Russ.).
  11. Tablitcy i matematicheskoe obespechenie spektralnogo metoda teorii avtomaticheskogo upravleniia [Tables and Mathware for the Spectral Method of Control Theory]. Moscow, Bauman Moscow State Technical University Publ., 1973. (In Russ.).
  12. Kloeden P.E., Platen E. Numerical Solution of Stochastic Differential Equations. Springer, 1995.
  13. Kuznetsov D.F. Mean-square approximation of iterated Ito and Stratonovich stochastic integrals: Method of generalized multiple Fourier series. Application to numerical integration of Ito SDEs and Semilinear SPDEs. Differencialnie Uravnenia i Protsesy Upravlenia = Differential Equations and Control Processes, 2023, no. 1, pp. A.1–A.947.
  14. Kuznetsov M.D., Kuznetsov D.F. SDE-MATH: A software package for the implementation of strong high-order numerical methods for Ito SDEs with multidimensional non-commutative noise based on multiple Fourier–Legendre series // Differencialnie Uravnenia i Protsesy Upravlenia = Differential Equations and Control Processes, 2021, no. 1, pp. 93–422.
  15. Milstein G.N., Tretyakov M.V. Stochastic Numerics for Mathematical Physics. Springer-Verlag, 2004.
  16. Rybakov K.A. Using spectral form of mathematical description to represent Stratonovich iterated stochastic integrals. Smart Innovation, Systems and Technologies. Vol. 217. Springer, 2021, pp. 287–304.
  17. Rybakov K.A. Using spectral form of mathematical description to represent Ito iterated stochas-tic integrals. Smart Innovation, Systems and Technologies. Vol. 274. Springer, 2022, pp. 331–344.

Information About the Authors

Konstantin A. Rybakov, PhD in Physics and Matematics, Associate Professor, Department of Mathematical Cybernetics, Moscow Aviation Institute (National Research University), Moscow, Russia, ORCID: https://orcid.org/0000-0002-6839-1090, e-mail: rkoffice@mail.ru

Metrics

Views

Total: 61
Previous month: 12
Current month: 0

Downloads

Total: 28
Previous month: 3
Current month: 0