Automatic code generation for stochastic Runge-Kutta methods

In this paper we consider in detail the realization of Runge-Kutta stochastic numerical methods with weak and strong convergence for systems of stochastic differential equations in Ito form. The algorithm for generating the Wiener stochastic process, the algorithm for approximation of Ito stochastic integrals, and the code generation algorithms for numerical schemes are described. Python and Julia languages are used. The Jinja2 template engine is used for the code generation . © 2018 CEUR-WS. All Rights Reserved.

Authors
Gevorkyan M.N. 1 , Demidova A.V. 1 , Korolkova A.V. 1 , Kulyabov D.S. 1, 2
Conference proceedings
CEUR Workshop Proceedings
Publisher
CEUR-WS
Language
English
Pages
90-100
Status
Published
Volume
2177
Year
2018
Organizations
  • 1 Department of Applied Probability and Informatics, Peoples' Friendship University of Russia (RUDN University), 6 Miklukho-Maklaya str., Moscow, 117198, Russian Federation
  • 2 Laboratory of Information Technologies, Joint Institute for Nuclear Research, 6 Joliot-Curie, Dubna, Moscow region, 141980, Russian Federation
Keywords
Automatic code generation; Julia; Python; Stochastic differential equations; Stochastic numeric methods; The template engine.
Date of creation
19.07.2019
Date of change
01.03.2021
Short link
https://repository.rudn.ru/en/records/article/record/38451/
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