# Finite difference approximations¶

## Initial-boundary value problems¶

Differential equations are mathematical models that describe physical systems arising in many science and engineering fields such as geoscience, climate modeling, fluid and solid mechanics, general relativity, materials science, electromagnetics, and medicine. A major task of scientific computing is finding approximate solutions to differential equations.

As an example, wave equarion is a partial differential equation used to model the propagation of waves in a medium. When the medium is homogeneous, the wave equation reads:

$$\frac{\partial^2 u}{\partial t^2} = c^2 \left( \frac{\partial^2 u}{\partial x^2} + \frac{\partial^2 u}{\partial y^2} + \frac{\partial^2 u}{\partial z^2} \right).$$

The solution $$u = u(x,y,z,t)$$ to the wave equation is a function of space ($$x,y,z$$) and time ($$t$$) and may represent the displacement in a solid, sound pressure, particle velocity, or, under certain conditions, the electric or magnetic fields. The quantity $$c$$ is the “wave speed” in the medium. Note that all derivatives are partial derivatives.

The above equation is in (3+1) dimensions: 3 spatial dimensions and 1 temporal dimension, referred to as 3D wave equation. In a single spatial dimension (for instance the vibration of a guitar string) the 1D wave equation takes the form

\begin{eqnarray} \frac{\partial^2 u(x,t)}{\partial t^2} = c^2 \frac{\partial^2 u(x,t)}{\partial x^2}, \qquad x \in [X_L,X_R], \ \ t>0, \\ u(x,0) = f_0(x), \ \ \frac{\partial u(x,0)}{\partial t} = f_1(x), \\ u(X_L,t) = g_L(t), \ \ u(X_R,t) = g_R(t). \end{eqnarray}

Here we have augmented the wave equation with two initial conditions (at $$t = 0$$) and two boundary conditions (at the left and right boundaries). In general, a partial differential equation (e.g. wave equation) together with its corresponding intial and boundary conditions is called an initial-boundary value problem.

## Numerical solutions to initial-boundary value problems¶

Many real-world problems cannot be solved by traditional experimental or theoretical tools, either because it is impossible (e.g. prediction of climate change), or because it is too expensive or time-consuming (e.g. determination of structure of proteins), or because experimentation is dangerous (e.g. study of toxic materials and explosions). As the third pillar of science, scientific computing tries to numerically solve complex scientific problems using computers.

As computers have finite amount of memory we will have to restrict ourselves to finding approximate solutions to the wave equation at a finite number of $$x \in [X_L, X_R]$$ and $$t \in [0, T]$$ values. One approach to do this is called finite difference approximation. There are many other approaches such as the finite element method, fininte volume method, and spectral method. Here we focus on the finite difference method.

In this section we will learn how to approximate the spatial derivative on the right hand side of the 1D wave equation. In close future we will learn how to solve the whole problem and evolve the solution in both time and space. Therefore, without loss of generality, let us drop $$t$$ for now and write $$u = u(x)$$. Note that it is only for notational convenience that we suppress the dependence on time.

### Finite differencing on uniform grids¶

First, we need to choose where (i.e. “for what values of $$x \in [X_L,X_R]$$“) we would like to know the approximate value of the derivative $$\frac{\partial^2 u(x)}{\partial x^2}$$. A natural choice is at a set of $$n+1$$ grid points on a equidistant grid

$$x_i = X_L + i h, \qquad i = 0,\ldots,n, \qquad h = \frac{X_R-X_L}{n}.$$

Here $$h$$ is the grid spacing (known as grid length or grid size) which has been chosen such that $$x_0 = X_L$$ and $$x_n = X_R$$.

Next, in order to find an approximation to the second derivative we expand the solution in a Taylor series around $$x$$ (at $$x+h$$ and $$x-h$$):

\begin{eqnarray} u(x+h) = u(x) + h \frac{\partial u(x)}{\partial x} + h^2/2 \frac{\partial^2 u(x)}{\partial x^2} + h^3/6 \frac{\partial^3 u(x)}{\partial x^3} + h^4/24 \frac{\partial^4 u(x)}{\partial x^4} + \mathcal{O}(h^5), \\ u(x-h) = u(x) - h \frac{\partial u(x)}{\partial x} + h^2/2 \frac{\partial^2 u(x)}{\partial x^2} - h^3/6 \frac{\partial^3 u(x)}{\partial x^3} + h^4/24 \frac{\partial^4 u(x)}{\partial x^4} + \mathcal{O}(h^5). \end{eqnarray}

Adding these equations together we get:

$$u(x+h) + u(x-h) = 2 u(x) + h^2 \frac{\partial^2 u(x)}{\partial x^2} + h^4/12 \frac{\partial^4 u(x)}{\partial x^4} + \mathcal{O}(h^5),$$

which can be rearranged to

$$\frac{u(x+h) -2 u(x) + u(x-h)}{h^2} - \frac{\partial^2 u(x)}{\partial x^2} = h^2/12 \frac{\partial^4 u(x)}{\partial x^4} + \mathcal{O}(h^3).$$

We therefore obtain

$$\frac{\partial^2 u(x)}{\partial x^2} = \frac{u(x+h) -2 u(x) + u(x-h)}{h^2} + \mathcal{O}(h^2).$$

The quotient on the right hand side is thus a second order accurate approximation to $$\frac{\partial^2 u(x)}{\partial x^2}$$. In particular we see that if we choose $$x=x_i$$ and use the fact that $$x_i \pm h = x_{i \pm 1}$$ and also introduce the notation $$u_i = u(x_i)$$ we may write:

$$\frac{\partial^2 u_i}{\partial x^2} = \frac{\partial^2 u(x_i)}{\partial x^2} \approx \frac{u(x_{i+1}) -2 u(x_i) + u(x_{i-1})}{h^2} = \frac{u_{i+1} -2 u_i + u_{i-1}}{h^2}.$$

This is called a central three-point finite difference approximation for the second derivative.

### Biased stencils¶

Assuming that we know $$u_i, \, i = 0,\ldots,n$$, we see that it is not possible to use the above centeral finite difference stencil at the boundary points $$x_0$$ and $$x_n$$. Using the central stencil at the end points would require knowing the solution $$u$$ at $$x_{-1}$$ and $$x_{n+1}$$ which are outside the domain. Instead we can derive biased (or one-sided) stencils at the end points of the grid. We again use Taylor’s theorem, but now we expand in Taylor series the interior solutions around end points. Expanding the interior solutions $$u_1$$ and $$u_2$$ around the left end-point $$x_0$$, we obtain:

\begin{eqnarray} u_1 = u_0 + h \frac{\partial u_0}{\partial x} + h^2/2 \frac{\partial^2 u_0}{\partial x^2} + h^3/6 \frac{\partial^3 u_0}{\partial x^3} + h^4/24 \frac{\partial^4 u_0}{\partial x^4} + \mathcal{O}(h^5), \\ u_2 = u_0 + 2h \frac{\partial u_0}{\partial x} + 4h^2/2 \frac{\partial^2 u_0}{\partial x^2} + 8h^3/6 \frac{\partial^3 u_0}{\partial x^3} + 16h^4/24 \frac{\partial^4 u_0}{\partial x^4} + \mathcal{O}(h^5). \end{eqnarray}

Now we can subtract two times the first equation form the second equation yielding the first order accurate approximation

$$\frac{u_2 - 2u_1 + u_0}{h^2} = \frac{\partial^2 u_0}{\partial x^2} + \mathcal{O}(h).$$

If we also include the expansion for $$u_3$$ we can get the second order approximation

$$\frac{-u_3 + 4u_2 - 5u_1 + 2u_0}{h^2} = \frac{\partial^2 u_0}{\partial x^2} + \mathcal{O}(h^2).$$

To check the coefficients of the stencils we can consider the special cases of a constant and a linear function which should be differentiated exactly by a first and second order accurate method respectively. Obviously both stencils are exact for a constant function $$u(x) = c$$ (the coefficients add up to zero). Additionally the second approximation is exact for the linear function $$u(x)=x$$ on the grid $$x_0 = 0, x_1 = 1, x_2 = 2, x_3 = 3$$ with $$h=1$$. The second derivative of a linear is zero which is exactly what comes out, i.e. $$-3 + 2\times 4 - 5\times 1 + 1\times 0 = 0$$.

Similarly, we can obtain the second order finite difference approximation for the second derivative at the right end-point:

$$\frac{-u_{n-3} + 4u_{n-2} - 5u_{n-1} + 2u_n}{h^2} = \frac{\partial^2 u_n}{\partial x^2} + \mathcal{O}(h^2).$$

### Higher order approximations¶

The above procedure can in principle be extended to use more points on the grid (instead of only 3 or 4 points), yielding higher order accurate approximations (higher than second-order accuracy). For a good list of stencil weights see here. A general algorithm for finding finite difference stencils of all orders has been found by Bengt Fornberg and is described in his excellent review paper.

In what follows we discus how the above approximations can be implemented in Fortan.

## Finite difference examples in Fortran¶

This section discusses implementations of the finite difference approximations discussed above. The Fortran codes for the following examples can be found in the course repository in directory “Labs/lab3”.

Here we only consider the approximation of the first-order derivative. Higher order derivatives can be computed similarly. We present three examples. In the first example we approximate the derivative using a three point second order accurate stencil on an equidistant grid. In the second example we use Bengt Fornberg’s subroutine Fornberg_weights.f90 to find the stencil that extends over all grid-points. This would, in principle, give an approximation of maximal order. However, we will find that this approach does not work well on equidistant grids. In the third example we investigate the performance of the maximal-order finite difference stencil (usually called spectral finite differences) on graded Legendre-Gauss-Lobatto grids. Recall that we have already discussed such grids in Homework 3 in the context of numerical integration.

### Example 1: a low-order finite difference method¶

Consider the function $$f(x) = e^{ \cos (\pi x)}$$ and its first derivative $$f'(x) = -\pi \sin (\pi x) e^{\cos (\pi x)}$$ where $$x\in[-1,1]$$. We further consider the equidistant grid

$$x_i = -1 + i h, \qquad i = 0,\ldots,n, \qquad h = \frac{2}{n},$$

and use Taylor series (similar to what we did above) to approximate the first derivative of $$f$$ at $$x_i$$, i.e. $$f'(x_i)$$, denoted by $$(f')_i$$:

\begin{eqnarray} (f')_i = \left\{ \begin{array}{ll} \frac{-1.5 f_{i}+2f_{i+1}-0.5 f_{i+2}}{h} & i = 0\\ \frac{0.5 f_{i+1}- 0.5 f_{i-1}}{h} & i = 1,\ldots,n-1\\ \frac{1.5 f_{i}-2f_{i-1} + 0.5 f_{i-2}}{h} & i = n \end{array} \right. \end{eqnarray}

Since we know the exact value of the derivative, we can compute the exact error in our finite difference approximation. Hence, using a decreasing sequence of $$h$$ values, we can easily check if the error decreases as we expect. We notice that since we will have different number of grid-points for different $$h$$ values, we declare the array x to be a 1D allocatable array. We also declare the arrays holding the function, the approximation to the derivative, and the exact derivative as allocatable (see line 6). The finite difference stencils on the other hand will be the same independent of the number of grid points and can thus be stored in a fixed size array diff_weights(1:3,1:3). Recall that in Fortran the default indexation starts with one, and hence an equivalent declaration would be diff_weights(3,3).

The code loops over all $$n=4,5,\ldots,100$$ and allocates arrays of size $$n+1$$ (line 10), computes the grid spacing, the grid, the function, and the exact derivative (lines 12-18). We then set up the array that holds the finite difference weights (note that due to the $$h$$ dependency of the weights, we have to change the weights for every loop) and compute the finite difference approximation to $$f'(x)$$ (lines 23-45). Finally, on line 46 we compute the maximum error on the grid and print it to screen.

  1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 program differentiate1 implicit none real(kind = 8), parameter :: pi = acos(-1.d0) integer :: n,i,j real(kind = 8) :: h,diff_weights(1:3,1:3) real(kind = 8), dimension(:), allocatable :: x,f,df,df_exact do n = 4,100 ! Allocate memory for the various arrays allocate(x(0:n),f(0:n),df(0:n),df_exact(0:n)) ! Set up the grid. h = 2.d0/dble(n) do i = 0,n x(i) = -1.d0+dble(i)*h end do ! The function and the exact derivative f = exp(cos(pi*x)) df_exact = -pi*sin(pi*x)*exp(cos(pi*x)) ! Set up weights for a finite difference stencil ! using three gridpoints ! In the interior we use a centered stencil diff_weights(1:3,1) = (/-0.5d0,0.d0,0.5d0/) ! To the left we use a biased stencil diff_weights(1:3,2) = (/-1.5d0,2.d0,-0.5d0/) ! To the right we use a biased stencil diff_weights(1:3,3) = (/1.5d0,-2.d0,0.5d0/) ! scale by 1/h diff_weights = diff_weights/h ! Now differentiate ! To the left is a special case df(0) = 0.d0 do j = 1,3 df(0) = df(0) + diff_weights(j,2)*f(j-1) end do ! Now interior points do i = 1,n-1 df(i) = sum(diff_weights(1:3,1)*f((i-1):(i+1))) end do ! Finally, special case to the right df(n) = 0.d0 do j = 1,3 df(n) = df(n) + diff_weights(j,3)*f(n-(j-1)) end do write(*,'(I3,2(ES12.4))') n,h,maxval(abs(df-df_exact)) ! Deallocate the arrays deallocate(x,f,df,df_exact) end do end program differentiate1 

The results from the output of the above program is displayed in the figure below. As we see the slope of the error is the same as a $$h^{2} \sim n^{-2}$$ indicating that the implementation may be correct (confirming that convergence is second order).

### Example 2: maximum order on an equidistant grid¶

Next, we use a finite difference stencil that extends over all $$n+1$$ grid-points to approximate the first derivative of $$f$$. The weights of the stencil are computed by Bengt Fornberg’s weights subroutine Fornberg_weights.f90, which is located in the course repository in directory “Labs/lab3”.

  1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 ! This program differentiates functions (up to m-th derivative) ! using a finite difference stencil with maximum width ! computed by Bengt Fornberg's weights subroutine (Fornberg_weights.f90) ! on an equidistant grid program differentiate2 implicit none real(kind = 8), parameter :: pi = acos(-1.d0) integer :: n,i,j,m real(kind = 8) :: h,z real(kind = 8), dimension(:), allocatable :: x,f,df,df_exact real(kind = 8), dimension(:,:), allocatable :: c m = 1 !the highest derivative (here 1) do n = 4,100 ! Allocate memory for various arrays allocate(x(0:n),f(0:n),df(0:n),df_exact(0:n)) allocate(c(0:n,0:m)) ! Set up the grid. h = 2.d0/dble(n) do i = 0,n x(i) = -1.d0+dble(i)*h end do ! The function and the exact derivative f = exp(cos(pi*x)) df_exact = -pi*sin(pi*x)*exp(cos(pi*x)) ! Find the difference stencil that uses all grid points do i = 0,n df(i) = 0.d0 z = x(i) call Fornberg_weights(z,x,n,m,c) do j = 0,n df(i) = df(i) + c(j,1)*f(j) end do end do write(*,'(ES12.4)') maxval(abs(df-df_exact)) ! Deallocate the arrays deallocate(x,f,df,df_exact,c) end do end program differentiate2 

The results from the output of the above program is displayed in the figure below, together with the results obtained from the first example. We observe that for small number of grid points, the wide stencil may deliver better results, compared to the three-point stencil. However, for large number of grid points, wide stencils have poor performance.

### Example 3: maximum order on a Legendre-Gauss-Lobatto grid¶

Finaly, we use Bengt Fornberg’s wide stencil on a Legendre-Gauss-Lobatto grid.

  1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 ! This program differentiates functions (up to m-th derivative) ! using a finite difference stencil with maximum width ! computed by Bengt Fornberg's weights subroutine (Fornberg_weights.f90) ! on a Legendre-Gauss-Lobatto grid computed by the subroutine lglnodes.f90 program differentiate3 implicit none real(kind = 8), parameter :: pi = acos(-1.d0) integer :: n,i,j,m real(kind = 8) :: z real(kind = 8), dimension(:), allocatable :: x,f,df,df_exact,w real(kind = 8), dimension(:,:), allocatable :: c m = 1 !the highest derivative (here 1) do n = 4,100 ! Allocate memory for various arrays allocate(x(0:n),f(0:n),df(0:n),df_exact(0:n),w(0:n)) allocate(c(0:n,0:m)) ! Set up the grid. call lglnodes(x,w,n) ! The function and the exact derivative f = exp(cos(pi*x)) df_exact = -pi*sin(pi*x)*exp(cos(pi*x)) ! Find the difference stencil that uses all grid points do i = 0,n df(i) = 0.d0 z = x(i) call Fornberg_weights(z,x,n,m,c) do j = 0,n df(i) = df(i) + c(j,1)*f(j) end do end do write(*,'(ES12.4)') maxval(abs(df-df_exact)) ! Deallocate the arrays deallocate(x,f,df,df_exact,c,w) end do end program differentiate3 

The results from the output of the above program is displayed in the figure below, together with the results obtained from the first two examples. We observe that the wide stencil has an excellent performance on LGL nodes, as it does not suffer from roundoff due to ill-conditioning that occur on equidistant grids. In general, LGL nodes deliver much better grid distributions than uniform distributions.