2021
DOI: 10.11648/j.ijssam.20210601.11
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Numerical Solution of First Order Ordinary Differential Equation by Using Runge-Kutta Method

Abstract: In this paper, the classical fourth-order Runge-Kutta methodis presented for solving the first-order ordinary differential equation. First, the given solution domain is discretizedby using a uniform discretization grid point. Next by applyingthe forward difference method, we discretized the given ordinary differential equation. And formulating a difference equation. Then using this difference equation, the given first-order ordinary differential equation is solved by using the classicalfourth-order Runge-Kutta… Show more

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Cited by 4 publications
(3 citation statements)
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“…To obtain a two-step predictor solution for first equation of set (30) for the non-linear plant virus propagation model by vector, use the following expression:…”
Section: Adams Methodsmentioning
confidence: 99%
“…To obtain a two-step predictor solution for first equation of set (30) for the non-linear plant virus propagation model by vector, use the following expression:…”
Section: Adams Methodsmentioning
confidence: 99%
“…The fundamental laws of physics, mechanics, electricity, and thermodynamics as well as the modeling of population growth and expansion are incorporated in these numerous situations (Al-Jawary et al, 2020;Rabiei et al, 2023). ODes can be found in a wide variety of applications across the fields of mathematics, social sciences, and natural sciences (Koroche, 2021). Most scholars have been interested in RKMs since the invention of digital computers, and a great number of researchers have contributed to recent developments of the theory and the development of expanded RKMs (Karthick et al, 2023;Batiha et al, 2023;Gebregiorgis and Gonfa, 2021).…”
Section:  mentioning
confidence: 99%
“…Runge-Kutta method. For the numerical solution of a differential equation, the Runge Kutta method of various orders could be adopted.The Runge Kutta first order are often referred to as the Euler forward method, where the derivatives of y at the given time step are used to do the extrapolations of the solution at the next time step [53]. On the other hand, the Runge-Kutta methods extrapolates the solution to the future time step using the information on the 'slope' at more than one point.…”
Section: Model Analysismentioning
confidence: 99%