Performance of 5-stage, 4-stage and specific stochastic Runge-Kutta methods in approximating the solution of stochastic biological model

In recent years, the transition on modelling physical systems via stochastic differential equations (SDEs) has attracted great interest among researchers. This is due to the limitations of ordinary differential equations in presenting the real phenomenon. To the fact that the stochastic models incor...

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Published in:Journal of Physics: Conference Series
Main Author: Ariffin N.A.N.; Rosli N.; Kasim A.R.M.; Mazlan M.S.A.
Format: Conference paper
Language:English
Published: IOP Publishing Ltd 2021
Online Access:https://www.scopus.com/inward/record.uri?eid=2-s2.0-85114212271&doi=10.1088%2f1742-6596%2f1988%2f1%2f012008&partnerID=40&md5=c690d20b44232a8d48a5b0d6ac8f1950
id 2-s2.0-85114212271
spelling 2-s2.0-85114212271
Ariffin N.A.N.; Rosli N.; Kasim A.R.M.; Mazlan M.S.A.
Performance of 5-stage, 4-stage and specific stochastic Runge-Kutta methods in approximating the solution of stochastic biological model
2021
Journal of Physics: Conference Series
1988
1
10.1088/1742-6596/1988/1/012008
https://www.scopus.com/inward/record.uri?eid=2-s2.0-85114212271&doi=10.1088%2f1742-6596%2f1988%2f1%2f012008&partnerID=40&md5=c690d20b44232a8d48a5b0d6ac8f1950
In recent years, the transition on modelling physical systems via stochastic differential equations (SDEs) has attracted great interest among researchers. This is due to the limitations of ordinary differential equations in presenting the real phenomenon. To the fact that the stochastic models incorporate the random effects that may influence the behaviour of physical systems, SDEs seems to be the best model that can be used i n assessing those systems. The growing interest among researchers in modelling the systems via SDEs comes with the rise in the need of numerical methods to approximate the solutions for SDEs. This is because by taking into account the random fluctuations in SDEs resulting to the complexity of finding the exact solution of SDEs. Therefore, it contribute to the increasing number of research to decide on the best numerical approach to solve the systems of SDEs. This paper is devoted to investigate the performance of 5-stage stochastic Runge-Kutta ( SRK5) with order 2.0, 4-stage stochastic Runge-Kutta ( SRK4), specific stochastic Runge-Kutta with order 1.5 ( SRKS1.5) and commutative specific stochastic Runge-Kutta with order 1.5 (SRKST2) in approximating the solution of stochastic model in biological system. A comparative study of SRK5, SRK4, SRKS1.5 and SRKST2 methods will be presented in this paper. The linear SDE model and the stochastic model of C. Acetobutylicum cell growth will be used to examine the performance of those methods and the numerical experiment will be conducted. The numerical solutions obtained will be discussed. © Published under licence by IOP Publishing Ltd.
IOP Publishing Ltd
17426588
English
Conference paper
All Open Access; Gold Open Access
author Ariffin N.A.N.; Rosli N.; Kasim A.R.M.; Mazlan M.S.A.
spellingShingle Ariffin N.A.N.; Rosli N.; Kasim A.R.M.; Mazlan M.S.A.
Performance of 5-stage, 4-stage and specific stochastic Runge-Kutta methods in approximating the solution of stochastic biological model
author_facet Ariffin N.A.N.; Rosli N.; Kasim A.R.M.; Mazlan M.S.A.
author_sort Ariffin N.A.N.; Rosli N.; Kasim A.R.M.; Mazlan M.S.A.
title Performance of 5-stage, 4-stage and specific stochastic Runge-Kutta methods in approximating the solution of stochastic biological model
title_short Performance of 5-stage, 4-stage and specific stochastic Runge-Kutta methods in approximating the solution of stochastic biological model
title_full Performance of 5-stage, 4-stage and specific stochastic Runge-Kutta methods in approximating the solution of stochastic biological model
title_fullStr Performance of 5-stage, 4-stage and specific stochastic Runge-Kutta methods in approximating the solution of stochastic biological model
title_full_unstemmed Performance of 5-stage, 4-stage and specific stochastic Runge-Kutta methods in approximating the solution of stochastic biological model
title_sort Performance of 5-stage, 4-stage and specific stochastic Runge-Kutta methods in approximating the solution of stochastic biological model
publishDate 2021
container_title Journal of Physics: Conference Series
container_volume 1988
container_issue 1
doi_str_mv 10.1088/1742-6596/1988/1/012008
url https://www.scopus.com/inward/record.uri?eid=2-s2.0-85114212271&doi=10.1088%2f1742-6596%2f1988%2f1%2f012008&partnerID=40&md5=c690d20b44232a8d48a5b0d6ac8f1950
description In recent years, the transition on modelling physical systems via stochastic differential equations (SDEs) has attracted great interest among researchers. This is due to the limitations of ordinary differential equations in presenting the real phenomenon. To the fact that the stochastic models incorporate the random effects that may influence the behaviour of physical systems, SDEs seems to be the best model that can be used i n assessing those systems. The growing interest among researchers in modelling the systems via SDEs comes with the rise in the need of numerical methods to approximate the solutions for SDEs. This is because by taking into account the random fluctuations in SDEs resulting to the complexity of finding the exact solution of SDEs. Therefore, it contribute to the increasing number of research to decide on the best numerical approach to solve the systems of SDEs. This paper is devoted to investigate the performance of 5-stage stochastic Runge-Kutta ( SRK5) with order 2.0, 4-stage stochastic Runge-Kutta ( SRK4), specific stochastic Runge-Kutta with order 1.5 ( SRKS1.5) and commutative specific stochastic Runge-Kutta with order 1.5 (SRKST2) in approximating the solution of stochastic model in biological system. A comparative study of SRK5, SRK4, SRKS1.5 and SRKST2 methods will be presented in this paper. The linear SDE model and the stochastic model of C. Acetobutylicum cell growth will be used to examine the performance of those methods and the numerical experiment will be conducted. The numerical solutions obtained will be discussed. © Published under licence by IOP Publishing Ltd.
publisher IOP Publishing Ltd
issn 17426588
language English
format Conference paper
accesstype All Open Access; Gold Open Access
record_format scopus
collection Scopus
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