On composite formulas for mathematical expectation of functionals of solution of the Ito equation in Hilbert space
https://doi.org/10.29235/1561-2430-2019-55-2-158-168
Abstract
This article is devoted to constructing composite approximate formulas for calculation of mathematical expectation of nonlinear functionals of solution of the linear Ito equation in Hilbert space with additive noise. As the leading process, the Wiener process taking values in Hilbert space is examined. The formulas are a sum of the approximations of the nonlinear functionals obtained by expanding the leading random process into a series of independent Gaussian random variables and correcting approximating functional quadrature formulas that ensure an approximate accuracy of compound formulas for third-order polynomials. As a test example, the application of the obtained formulas to the case of a one-dimensional wave equation with a leading Wiener process indexed by spatial and temporal variables is considered. This article continues the research begun in [1].
The problem is motivated by the necessity to calculate the nonlinear functionals of solution of stochastic partial differential equations. Approximate evaluation of mathematical expectation of stochastic equations with a leading random process indexed only by the time variable is considered in [2–11]. Stochastic partial equations in various interpretations are considered [12–16]. The present article uses the approach given in [12].
About the Author
A. D. EgorovBelarus
Alexandr D. Egorov – Dr. Sc. (Physics and Mathematics), Chief Researcher
11, Surganov Str., 220072, Minsk, Republic of Belarus
References
1. Egorov A. D. Approximate formulas for calculating the mathematical expectation of functionals of solution of the Ito equations in a Hilbert space. Doklady Natsional’noi akademii nauk Belarusi = Doklady of the National Academy of Sciences of Belarus, 2016, vol. 60, no. 6, pp. 7–13 (in Russian).
2. Egorov A. D., Sobolevsky P. I., Yanovich L. A. Functional Integrals: Approximate Evaluations and Applications. Kluwer Academic Publishers, 1993. https://doi.org/10.1007/978-94-011-1761-6
3. Kloeden P. E., Platen E. Numerical Solution of Stochastic Differential Equations. Berlin, Springer Science & Business Media, 2013. 636 p.
4. Egorov A. D., Zhidkov E. P., Lobanov Yu. Yu. Introduction to Theory and Applications of Functional Integration. Moscow, Fizmatlit Publ., 2006. 400 p. (in Russian).
5. Egorov A. D., Zherelo A. V. Approximations of functional integrals with respect to measure generated by solutions of stochastic differential equations. Monte Carlo Methods and Applications, 2004, vol. 10, no. 3–4, pp. 257–264. https://doi.org/10.1515/mcma.2004.10.3-4.257
6. Egorov A. D. Approximations for expectation of functionals of solutions to stochastic differential equations. Monte Carlo Methods and Applications, 2007, vol. 13, no. 4, pp. 275–185. https://doi.org/10.1515/mcma.2004.10.3-4.257
7. Egorov A. D., Sabelfeld K. K. Approximate formulas for expectation of functionals of solutions to stochastic differential equations. Monte Carlo Methods and Applications,2010, vol. 16, no. 2, pp. 95–127. https://doi.org/10.1515/mcma.2010.003
8. Milstein G. N., Tretyakov M. V. Evaluation of conditional Wiener integrals by numerical integration of stochastic differential equations, Journal of Computational Physics, 2004, vol. 197, no. 1, pp. 275–298. https://doi.org/10.1016/j.jcp.2003.12.001
9. Dumas W. M., Tretyakov M. V. Computing conditional Wiener integrals of functional of a general form. IMA Journal of Numerical Analysis, 2011, vol. 31, no. 3, pp. 1217–1251. https://doi.org/10.1093/imanum/drq008
10. Airjan E. A., Egorov A. D., Kulyabov D. S., Malyutin V. B., Sevastyanov L. A. Application of functional integrals to stochastic equations. Matematicheskoe modelirovanie Mathematical Models and Computer Simulations,2017, vol. 9, no. 3, pp. 339–348. https://doi.org/10.1134/s2070048217030024
11. Egorov A., Malyutin V. A method for the calculation of characteristics for the solution to stochastic differential equations. Monte Carlo Methods and Applications,2017, vol. 23, no. 3, pp. 149–157. https://doi.org/10.1515/mcma-2017-0110
12. Da Prato G., Zabczyk J. Stochastic Equations in Infinite Dimensions. Cambridge University Press, 1992. 454 p. https://doi.org/10.1017/cbo9780511666223
13. Gavarecki G. V., Mandrekar V. Stochastic Differential Equations. Stochastic Differential Equations in Infinite Dimensions with Applications to Stochastic Partial Differential Equations. Berlin, Springer, 2011, pp. 73–149. https://doi.org/10.1007/978-3-642-16194-0_3
14. Dalang R. S., Khoshnevisan D., Mueller C., Nualart D., Xiao Y. A Minicourse on Stochastic Partial Differential Equations. Springer, 2006. 222 p.
15. Hairer M. An Introduction to Stochastic PDEs. The University of Warwick/Courant Institute, 2009. 78 p.
16. Jentzen A., Kloeden P. E. Taylor Approximations for Stochastic Partial Differential Equations. Philadelphia, SIAM Press, 2011. 235 p. https://doi.org/10.1137/1.9781611972016
17. Likhoded N. A. Refinement of the Monte Carlo estimate of continual integrals. Vescì Akademìì navuk BSSR. Seryâ fìzìka-matèmatyčnyh navuk [Proceedings of the Academy of Sciences of BSSR. Physics and Mathematics Series], 1990, no. 2, pp. 8–13 (in Russian).