AccScience Publishing / IJOCTA / Volume 10 / Issue 2 / DOI: 10.11121/ijocta.01.2020.00829
RESEARCH ARTICLE

Qualitative behavior of stiff ODEs through a stochastic approach

Hande Uslu1 Murat Sari1* Tahir Cosgu1,2
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1 Department of Mathematics, Yildiz Technical University, Turkey
2 Department of Mathematics, Amasya University, Turkey
IJOCTA 2020, 10(2), 181–187; https://doi.org/10.11121/ijocta.01.2020.00829
Submitted: 5 June 2019 | Accepted: 20 December 2019 | Published: 5 June 2020
© 2020 by the Author(s). This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution -Noncommercial 4.0 International License (CC-by the license) ( https://creativecommons.org/licenses/by-nc/4.0/ )
Abstract

In the last few decades, stiff differential equations have attracted a great deal of interest from academic society, because much of the real life is covered by stiff behavior. In addition to importance of producing model equations, capturing an exact behavior of the problem by dealing with a solution method is also handling issue. Although there are many explicit and implicit numerical methods for solving them, those methods cannot be properly applied due to their computational time, computational error or effort spent for construction of a structure. Therefore, simulation techniques can be taken into account in capturing the stiff behavior. In this respect, this study aims at analyzing stiff processes through stochastic approaches. Thus, a Monte Carlo based algorithm has been presented for solving some stiff ordinary differential equations and system of stiff linear ordinary differential equations. The produced results have been qualitatively and quantitatively discussed.

Keywords
Stiff Differential Equation
Monte Carlo Method
Stochastic Approach
Conflict of interest
The authors declare they have no competing interests.
References

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[12] Sari, M., Uslu, H. & Cosgun, T. (2018). The Qualitative Behavior of Some Stiff ODEs Through Stochastic Methods. The International Conference on Applied Mathematics in Engineering (ICAME'18), Balikesir, Turkey.

[13] Uslu, H. & Sari, M. (2019). Monte Carlo based stochastic approach for first order nonlinear ODE systems. Pamukkale Journal of Engineering Sciences, 26(1), 133-139.

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An International Journal of Optimization and Control: Theories & Applications, Electronic ISSN: 2146-5703 Print ISSN: 2146-0957, Published by AccScience Publishing