A Complete Step-by-Step Guide on Euler’s Method

Euler's method is a numerical technique for approximating solutions to ordinary differential equations. It starts with an initial value and estimates the next point on the solution curve using the derivative at the current point. The method iteratively advances through small time steps, repeatedly applying this process. While simple to implement, Euler's method may introduce errors, especially with large step sizes, resulting in less accurate approximations. More refined numerical methods, such as the Runge-Kutta methods, offer improved accuracy by considering multiple points and slopes within each step.

A Complete Step-by-Step Guide on Euler’s Method

Euler’s method Steps:

These are the steps to find an approximate numerical solution to a first-order ordinary differential equation using Euler’s method without using mathematical symbols:

  1. Start with an Equation: Begin with a differential equation that describes how a variable changes concerning another variable.
  2. Choose a Range: Determine the range of values for the independent variable where you want to find the solution.
  3. Select a Step Size: Decide how small of a step you want to take within the chosen range. Smaller steps typically lead to more accurate results but require more calculations.
  4. Set Initial Values: Specify an initial value for the dependent variable at the starting point within the range.
  5. Perform Iterations:
    • Calculate the rate of change (slope) of the dependent variable at the current point.
    • Update the values of the independent and dependent variables based on the calculated slope and step size.
    • Repeat this process until you’ve covered the entire range by incrementing the independent variable at each step.
  6. Record Values: Keep track of the values of both the independent and dependent variables at each step of the iteration.
  7. Complete Iterations: Continue the iterations until you’ve covered the entire range specified in step 2.
  8. Analyze the Solution: Examine the recorded values to understand how the dependent variable changes concerning the independent variable over the chosen range.
  9. Interpret the Results: Interpret the numerical solution in the context of the problem you’re trying to solve. Consider whether the results make sense and if adjustments are needed, such as changing the step size for greater accuracy.

Euler’s Method is used to find approximate solutions of first-order differential equations of the form \(\frac{dy}{dx} = f(x, y).\)

Step 1: Initial Condition

Start with an initial condition given as \((x_0, y_0).\)

Step 2: Step Size

Choose a step size \(h\), which determines how far to ‘step’ along the \(x-axis\) for each iteration.

Step 3: Iterative Formula

Apply Euler’s iterative formula:

\(y_{n+1} = y_n + h \cdot f(x_n, y_n)\),

where \(y_{n+1}\) is the next approximation, \(y_n\) is the current approximation, and \(f(x_n, y_n)\) is the value of the derivative at \((x_n, y_n).\)

Step 4: Repeat

Repeat the iterative formula for each step until the desired value of \(x\) is reached.

Step 5: Final Solution

The approximate value of \(y\) at the desired point is the last computed \(y_n.\)

Let’s take a look at an example:


Use Euler’s Method to approximate the solution of the differential equation

\( \frac{dy}{dx} = y – x \), with initial condition \( (x_0, y_0) = (0, 1) \) and step size \( h = 0.1 \).

The initial condition is given as \( (x_0, y_0) = (0, 1) \).

Choose a step size of \( h = 0.1 \).

Apply Euler’s formula: \( y_{n+1} = y_n + h \cdot f(x_n, y_n) \).

Here, \( f(x, y) = y – x \).

For the first iteration \((n = 0)\):

\( y_{1} = y_{0} + 0.1 \cdot (y_{0} – x_{0}) = 1 + 0.1 \cdot (1 – 0) = 1.1 \).

For the second iteration \((n = 1)\):

\( y_{2} = y_{1} + 0.1 \cdot (y_{1} – x_{1}) = 1.1 + 0.1 \cdot (1.1 – 0.1) = 1.2 \).

Continue this process for additional iterations.

After a desired number of iterations, the approximate value of \( y \) \text{ is obtained. For instance, after \(10\) iterations \((x = 1)\), the approximate value of \( y \) is calculated.

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