Euler's method for coupled ODE's

In summary, using the Euler integration method with a step-size of h=1, the entries in the table are as follows:t_k | y_1(t_k) | y_2(t_k)-------------------------0 | 1 | 11 | 2 | 02 | 2 | -23 | 0 | -4The equations used were y_{k+1} = y_{k} + f(t_{k},y_{k})h, where y is a vector and f is a vector-valued function. The initial conditions for y_1 and y_2 were y_1(0)=1 and y_2
  • #1
gfd43tg
Gold Member
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Homework Statement


Consider the following pair of coupled first order ODEs

[itex]\dot{y_{1}} = y_{2}[/itex] with ##y_{1}(0) = 1##
[itex]\dot{y_{2}} = -y_{1}[/itex] with ##y_{2}(0) = 1##

Use the Euler integration method with a step-size ##h = 1## and fill out the entries in the table below

[itex]\begin{bmatrix}
t_{k}&y_{1}(t_{k})&y_{2}(t_{k})\\
0 & &\\
1 & &\\
2 & &\\
3 & & -4\\
\end{bmatrix}[/itex]

Homework Equations


The Attempt at a Solution


Normally I understand how to do Euler's method, but of course now it's a coupled ODE so I am very confused how to do it. I know in general you do

##y_{k+1} = y_{k} + f(t_{k},y_{k})h##

But with the coupled ODE I am lost
 
Last edited:
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  • #2
Maylis said:

Homework Statement


Consider the following pair of coupled first order ODEs

[itex]\dot{y_{1}} = y_{2}[/itex] with ##y_{1}(0) = 1##
[itex]\dot{y_{2}} = -y_{1}[/itex] with ##y_{2}(0) = 1##

Use the Euler integration method with a step-size ##h = 1## and fill out the entries in the table below

[itex]\begin{bmatrix}
t_{k}&y_{1}(t_{k})&y_{2}(t_{k})\\
0 & &\\
1 & &\\
2 & &\\
3 & & -4\\
\end{bmatrix}[/itex]

Homework Equations





The Attempt at a Solution


Normally I understand how to do Euler's method, but of course now it's a coupled ODE so I am very confused how to do it. I know in general you do

In this equation:
##y_{k+1} = y_{k} + f(t_{k},y_{k})h##
[itex]y[/itex] can be (and normally is) a vector, and [itex]f[/itex] can be (and normally is) a vector-valued function.
 
  • #3
How does it look?

[itex]\begin{bmatrix}
t_{k}&y_{1}(t_{k})&y_{2}(t_{k})\\
0 & 1 &1\\
1 & 2 &0\\
2 & 2 & -2\\
3 & 0 & -4\\
\end{bmatrix}[/itex]

[itex] \begin{bmatrix} y_{{1},t_{k}=1} \\ y_{{2},t_{k}=1}\\ \end{bmatrix} = \begin{bmatrix} 1 \\ 1 \\ \end{bmatrix} + \begin{bmatrix}1\\-1\\ \end{bmatrix}*1 = \begin{bmatrix} 2 \\ 0 \\ \end{bmatrix} [/itex]

[itex] \begin{bmatrix} y_{{1},t_{k}=2} \\ y_{{2},t_{k}=2}\\ \end{bmatrix} = \begin{bmatrix} 2 \\ 0 \\ \end{bmatrix} + \begin{bmatrix}0\\-2\\ \end{bmatrix}*1 = \begin{bmatrix} 2\\ -2 \\ \end{bmatrix}[/itex]

[itex] \begin{bmatrix} y_{{1},t_{k}=3} \\ y_{{2},t_{k}=3}\\ \end{bmatrix} = \begin{bmatrix} 2 \\ -2 \\ \end{bmatrix} + \begin{bmatrix}-2\\-2\\ \end{bmatrix}*1 = \begin{bmatrix} 0\\ -4 \\ \end{bmatrix}[/itex]
 
Last edited:

Related to Euler's method for coupled ODE's

1. What is Euler's method for coupled ODE's?

Euler's method is a numerical method used to solve systems of coupled ordinary differential equations (ODE's). It is a first-order method that uses small time steps to approximate the solution of the ODE's.

2. How does Euler's method work?

Euler's method works by approximating the solution of a system of ODE's at discrete time steps. It starts with an initial value and uses the derivative of the function at that point to calculate the next value. This process is repeated until the desired number of time steps is reached.

3. What are the advantages of using Euler's method for coupled ODE's?

Euler's method is relatively simple to implement and does not require advanced mathematical techniques. It is also computationally efficient and can provide a good approximation of the solution for small time steps.

4. What are the limitations of Euler's method?

One of the main limitations of Euler's method is that it can introduce significant errors when used with larger time steps. This can result in inaccurate solutions and make it unsuitable for certain applications. Additionally, it is a first-order method, so it may not be as accurate as higher-order methods.

5. How can I improve the accuracy of Euler's method for coupled ODE's?

To improve the accuracy of Euler's method, you can use smaller time steps. However, this can significantly increase the computational cost. Another option is to use higher-order methods, such as the Runge-Kutta method, which can provide more accurate solutions with larger time steps.

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