# Modelling Gravity using the Euler Method in Haskell

## Introduction

In my predictive modelling class, we were given a homework assignment to apply the Euler method for a simple equation and then write a program to compute the method. I will show what I did to apply the Euler method, and then how I programmed it using Haskell.

## Our Equation

For our task we were given the simple equation to run the Euler method on:

\begin{equation} \frac{dv}{dt} = -g \end{equation}

where $$g=9.8 m/s^2$$ (this should look familiar to any physicists). This equation models the falling of an object (on Earth).

## Applying the Euler Method

To apply the Euler method, one must first have a base case. In this instance, assume that the initial velocity of the object is 0, thus when $$t = 0$$, $$v = 0$$; in other words $$v(0) = 0$$.

Now, from my understanding the Euler method is basically a recursive way to approximate the function. Thus, let $$h$$ be a given step size and $$v(t)$$ be the velocity at a given time $$t$$, and $$v_n$$ be the velocity at a given step $$n$$. It follows by Euler’s method that:

\begin{equation} v_{n+1} = v_n + hv(t) \end{equation}

In English, this equation is saying that the velocity at the next time step is equal to the velocity at the previous time step ($$v_n$$) plus the velocity of the current time ($$v(t)$$) times the step size ($$h$$). How do you compute the velocity at the previous time step ($$v_n$$)? Well, it is $$v_n = v(t - h)$$, the velocity at the previous time step.

## Translating Math into Haskell

Due to Haskell’s nature, it isn’t hard to translate mathematical equations into Haskell functions (if you have ever seen a Haskell function, you know what I mean). Because of the recursive nature, the reasoning behind the Haskell function is a little bit different than the mathematical intuition presented earlier, but it is the same principle.

First, let’s define $$g$$ in Haskell:

g :: Float
g = 9.8


This is pretty straightforward, as defined earlier $$g = 9.8$$ (the acceleration of gravity on Earth). If you are unfamiliar with Haskell, the first line describes the type of $$g$$ and the second line sets the value of $$g$$. Now let’s move onto the meat of the algorithm!

Forming the function step by step, the first step is to establish the types that our function will be dealing with:

v :: Float -> Float -> Float


Which means that our function v will take two parameters (that are of type Float), and return a value that is a Float.

Next, let’s create our function modelled after the formulae we presented earlier:

v :: Float -> Float -> Float
v t h = v_n + v_t
where v_n = v (t - h) h
v_t = h * (-g)


this should look very similar to the equation above. However, the acute observer will notice that this function is not complete because there is no base case for the recursion to break, thus this function will run forever!!

At the beginning we assumed that our base case was that the initial velocity was 0, thus adding this to our function we get:

v :: Float -> Float -> Float
v t h | t > 0 = v_n + v_t
| othwerwise = 0.0
where v_n = v (t - h) h
v_t = h * (-g)


In Haskell the | are called guards, which essentially translates into a conditional statement where the first expression that evaluates to true on the left hand side of the = returns what is on the right hand side of the =.

For example, if we test our function where $$t = 1$$ and $$h = 1$$, then the first time the function is called the first guard will evaluate to true (because $$t = 1; 1 > 0 = = True$$) thus the function is called again (inside v_n), but $$t = 0$$ so the second guard is reached. Ultimately the function will return $$9.8$$.

## Conclusion

This was my first encounter using the Euler method to do numerical analysis and model an equation. I hope that you found this interesting and that you enjoy the elegance of Haskell as much as I do!