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# Blog

2020-03-31
Recently, you've probably seen a lot of graphs that look like this:
The graph above shows something that is growing exponentially: its equation is $$y=kr^x$$, for some constants $$k$$ and $$r$$. The value of the constant $$r$$ is very important, as it tells you how quickly the value is going to grow. Using a graph of some data, it is difficult to get an anywhere-near-accurate approximation of $$r$$.
The following plot shows three different exponentials. It's very difficult to say anything about them except that they grow very quickly above around $$x=15$$.
$$y=2^x$$, $$y=40\times 1.5^x$$, and $$y=0.002\times3^x$$
It would be nice if we could plot these in a way that their important properties—such as the value of the ratio $$r$$—were more clearly evident from the graph. To do this, we start by taking the log of both sides of the equation:
$$\log y=\log(kr^x)$$
Using the laws of logs, this simplifies to:
$$\log y=\log k+x\log r$$
This is now the equation of a straight line, $$\hat{y}=m\hat{x}+c$$, with $$\hat{y}=\log y$$, $$\hat{x}=x$$, $$m=\log r$$ and $$c=\log k$$. So if we plot $$x$$ against $$\log y$$, we should get a straight line with gradient $$\log r$$. If we plot the same three exponentials as above using a log-scaled $$y$$-axis, we get:
$$y=2^x$$, $$y=40\times 1.5^x$$, and $$y=0.002\times3^x$$ with a log-scaled $$y$$-axis
From this picture alone, it is very clear that the blue exponential has the largest value of $$r$$, and we could quickly work out a decent approximation of this value by calculating 10 (or the base of the log used if using a different log) to the power of the gradient.

### Log-log plots

Exponential growth isn't the only situation where scaling the axes is beneficial. In my research in finite and boundary element methods, it is common that the error of the solution $$e$$ is given in terms of a grid parameter $$h$$ by a polynomial of the form $$e=ah^k$$, for some constants $$a$$ and $$k$$.
We are often interested in the value of the power $$k$$. If we plot $$e$$ against $$h$$, it's once again difficult to judge the value of $$k$$ from the graph alone. The following graph shows three polynomials.
$$y=x^2$$, $$y=x^{1.5}$$, and $$y=0.5x^3$$
Once again is is difficult to judge any of the important properties of these. To improve this, we once again begin by taking the log of each side of the equation:
$$\log e=\log (ah^k)$$
Applying the laws of logs this time gives:
$$\log e=\log a+k\log h$$
This is now the equation of a straight line, $$\hat{y}=m\hat{x}+c$$, with $$\hat{y}=\log e$$, $$\hat{x}=\log h$$, $$m=k$$ and $$c=\log a$$. So if we plot $$\log x$$ against $$\log y$$, we should get a straight line with gradient $$k$$.
Doing this for the same three curves as above gives the following plot.
$$y=x^2$$, $$y=x^{1.5}$$, and $$y=0.5x^3$$ with log-scaled $$x$$- and $$y$$-axes
It is easy to see that the blue line has the highest value of $$k$$ (as it has the highest gradient, and we could get a decent approximation of this value by finding the line's gradient.

As well as making it easier to get good approximations of important parameters, making curves into straight lines in this way also makes it easier to plot the trend of real data. Drawing accurate exponentials and polynomials is hard at the best of times; and real data will not exactly follow the curve, so drawing an exponential or quadratic of best fit will be an even harder task. By scaling the axes first though, this task simplifies to drawing a straight line through the data; this is much easier.
So next time you're struggling with an awkward curve, why not try turning it into a straight line first.

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2020-02-17
 This is the sixth post in a series of posts about my PhD thesis.
Before we move on to some concluding remarks and notes about possible future work, I must take this opportunity to thank my co-supervisors Timo Betcke and Erik Burman, as without their help, support, and patience, this work would never have happened.

### Future work

There are of course many things related to the work in my thesis that could be worked on in the future by me or others.
In the thesis, we presented the analysis of the weak imposition of Dirichlet, Neumann, mixed Dirichlet–Neumann, Robin, and Signorini boundary conditions on Laplace's equation; and Dirichlet, and mixed Dirichlet–Neumann conditions on the Helmholtz equation. One area of future work would be to extend this analysis to other conditions, such as the imposition of Robin conditions on the Helmholtz equation. It would also be of great interest to extend the method to other problems, such as Maxwell's equations. For Maxwell's equations, it looks like the analysis will be significantly more difficult.
In the problems in later chapters, in particular chapter 4, the ill-conditioning of the matrices obtained from the method led to slow or even inaccurate solutions. It would be interesting to look into alternative preconditioning methods for these problems as a way to improve the conditioning of these matrices. Developing these preconditioners appears to be very important for Maxwell's equations: general the matrices involved when solving Maxwell's equations tend to be very badly ill-conditioned, and in the few experiments I ran to try out the weak imposition of boundary conditions on Maxwell's equations, I was unable to get a good solution due to this.

If you are a undergraduate or master's student and are interested in working on similar stuff to me, then you could look into doing a PhD with Timo and/or Erik (my supervisors). There are also many other people around working on similar stuff, including:
There are of course many, many more people working on this, and this list is in no way exhaustive. But hopefully this list can be a useful starting point for anyone interested in studying this area of maths.
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2020-02-16
 This is the fifth post in a series of posts about my PhD thesis.
In the fifth and final chapter of my thesis, we look at how boundary conditions can be weakly imposed on the Helmholtz equation.

### Analysis

As in chapter 4, we must adapt the analysis of chapter 3 to apply to Helmholtz problems. The boundary operators for the Helmholtz equation satisfy less strong conditions than the operators for Laplace's equation (for Laplace's equation, the operators satisfy a condition called coercivity; for Helmholtz, the operators satisfy a weaker condition called Gårding's inequality), making proving results about Helmholtz problem harder.
After some work, we are able to prove an a priori error bound (with $$a=\tfrac32$$ for the spaces we use):
$$\left\|u-u_h\right\|\leqslant ch^{a}\left\|u\right\|$$

### Numerical results

As in the previous chapters, we use Bempp to show that computations with this method match the theory.
The error of our approximate solutions of a Dirichlet (left) and mixed Dirichlet–Neumann problems in the exterior of a sphere with meshes with different values of $$h$$. The dashed lines show order $$\tfrac32$$ convergence.

### Wave scattering

Boundary element methods are often used to solve Helmholtz wave scattering problems. These are problems in which a sound wave is travelling though a medium (eg the air), then hits an object: you want to know what the sound wave that scatters off the object looks like.
If there are multiple objects that the wave is scattering off, the boundary element method formulation can get quite complicated. When using weak imposition, the formulation is simpler: this one advantage of this method.
The following diagram shows a sound wave scattering off a mixure of sound-hard and sound-soft spheres. Sound-hard objects reflect sound well, while sound-soft objects absorb it well.
A sound wave scattering off a mixture of sound-hard (white) and sound-soft (black) spheres.
If you are trying to design something with particular properties—for example, a barrier that absorbs sound—you may want to solve lots of wave scattering problems on an object on some objects with various values taken for their reflective properties. This type of problem is often called an inverse problem.
For this type of problem, weakly imposing boundary conditions has advantages: the discretisation of the Calderón projector can be reused for each problem, and only the terms due to the weakly imposed boundary conditions need to be recalculated. This is an advantages as the boundary condition terms are much less expensive (ie they use much less time and memory) to calculate than the Calderón term that is reused.

This concludes chapter 5, the final chapter of my thesis. Why not celebrate reaching the end by cracking open the following figure before reading the concluding blog post.
An acoustic wave scattering off a sound-hard champagne bottle and a sound-soft cork.
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2020-02-13
 This is the fourth post in a series of posts about my PhD thesis.
The fourth chapter of my thesis looks at weakly imposing Signorini boundary conditions on the boundary element method.

### Signorini conditions

Signorini boundary conditions are composed of the following three conditions on the boundary:
\begin{align*} u &\leqslant g\\ \frac{\partial u}{\partial n} &\leqslant \psi\\ \left(\frac{\partial u}{\partial n} -\psi\right)(u-g) &=0 \end{align*}
In these equations, $$u$$ is the solution, $$\frac{\partial u}{\partial n}$$ is the derivative of the solution in the direction normal to the boundary, and $$g$$ and $$\psi$$ are (known) functions.
These conditions model an object that is coming into contact with a surface: imagine an object that is being pushed upwards towards a surface. $$u$$ is the height of the object at each point; $$\frac{\partial u}{\partial n}$$ is the speed the object is moving upwards at each point; $$g$$ is the height of the surface; and $$\psi$$ is the speed at which the upwards force will cause the object to move. We now consider the meaning of each of the three conditions.
The first condition ($$u \leqslant g$$ says the the height of the object is less than or equal to the height of the surface. Or in other words, no part of the object has been pushed through the surface. If you've ever picked up a real object, you will see that this is sensible.
The second condition ($$\frac{\partial u}{\partial n} \leqslant \psi$$) says that the speed of the object is less than or equal to the speed that the upwards force will cause. Or in other words, the is no extra hidden force that could cause the object to move faster.
The final condition ($$\left(\frac{\partial u}{\partial n} -\psi\right)(u-g)=0$$) says that either $$u=g$$ or $$\frac{\partial u}{\partial n}=\psi$$. Or in other words, either the object is touching the surface, or it is not and so it is travelling at the speed caused by the force.

### Non-linear problems

It is possible to rewrite these conditions as the following, where $$c$$ is any positive constant:
$$u-g=\left[u-g + c\left(\frac{\partial u}{\partial n}-\psi\right)\right]_-$$
The term $$\left[a\right]_-$$ is equal to $$a$$ if $$a$$ is negative or 0 if $$a$$ is positive (ie $$\left[a\right]_-=\min(0,a)$$). If you think about what this will be equal to if $$u=g$$, $$u\lt g$$, $$\frac{\partial u}{\partial n}=\psi$$, and $$\frac{\partial u}{\partial n}\lt\psi$$, you can convince yourself that it is equivalent to the three Signorini conditions.
Writing the condition like this is helpful, as this can easily be added into the matrix system due to the boundary element method to weakly impose it. There is, however, a complication: due to the $$[\cdot]_-$$ operator, the term we add on is non-linear and cannot be represented as a matrix. We therefore need to do a little more than simply use our favourite matrix solver to solve this problem.
To solve this non-linear system, we use an iterative approach: first make a guess at what the solution might be (eg guess that $$u=0$$). We then use this guess to calculate the value of the non-linear term, then solve the linear system obtained when the value is substituted in. This gives us a second guess of the solution: we can do follow the same approach to obtain a third guess. And a fourth. And a fifth. We continue until one of our guesses is very close to the following guess, and we have an approximation of the solution.

### Analysis

After deriving formulations for weakly imposed Signorini conditions on the boundary element method, we proceed as we did in chapter 3 and analyse the method.
The analysis in chapter 4 differs from that in chapter 3 as the system in this chapter is non-linear. The final result, however, closely resembles the results in chapter 3: we obtain and a priori error bounds:
$$\left\|u-u_h\right\|\leqslant ch^{a}\left\|u\right\|$$
As in chapter 3, The value of the constant $$a$$ for the spaces we use is $$\tfrac32$$.

### Numerical results

As in chapter 3, we used Bempp to run some numerical experiments to demonstrate the performance of this method.
The error of our approximate solutions of a Signorini problem on the interior of a sphere with meshes with different values of $$h$$ for two choices of combinations of discrete spaces. The dashed lines show order $$\tfrac32$$ convergence.
These results are for two different combinations of the discrete spaces we looked at in chapter 2. The plot on the left shows the expected order $$\tfrac32$$. The plot on the right, however, shows a lower convergence than our a priori error bound predicted. This is due to the matrices obtained when using this combination of spaces being ill-conditioned, and so our solver struggles to find a good solution. This ill-conditioning is worse for smaller values of $$h$$, which is why the plot starts at around the expected order of convergence, but then the convergence tails off.

These results conclude chapter 4 of my thesis. Why not take a break and snack on the following figure before reading on.
The solution of a mixed Dirichlet–Signorini problem on the interior of an apple, solved using weakly imposed boundary conditions.
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2020-02-11
 This is the third post in a series of posts about my PhD thesis.
In the third chapter of my thesis, we look at how boundary conditions can be weakly imposed when using the boundary element method. Weak imposition of boundary condition is a fairly popular approach when using the finite element method, but our application of it to the boundary element method, and our analysis of it, is new. But before we can look at this, we must first look at what boundary conditions are and what weak imposition means.

### Boundary conditions

A boundary condition comes alongside the PDE as part of the problem we are trying to solve. As the name suggests, it is a condition on the boundary: it tells us what the solution to the problem will do at the edge of the region we are solving the problem in. For example, if we are solving a problem involving sound waves hitting an object, the boundary condition could tell us that the object reflects all the sound, or absorbs all the sound, or does something inbetween these two.
The most commonly used boundary conditions are Dirichlet conditions, Neumann conditions and Robin conditions. Dirichlet conditions say that the solution has a certain value on the boundary; Neumann conditions say that derivative of the solution has a certain value on the boundary; Robin conditions say that the solution at its derivate are in some way related to each other (eg one is two times the other).

### Imposing boundary conditions

Without boundary conditions, the PDE will have many solutions. This is analagous to the following matrix problem, or the equivalent system of simultaneous equations.
\begin{align*} \begin{bmatrix} 1&2&0\\ 3&1&1\\ 4&3&1 \end{bmatrix}\mathbf{x} &= \begin{pmatrix} 3\\4\\7 \end{pmatrix} &&\text{or}& \begin{array}{3} &1x+2y+0z=3\\ &3x+1y+1z=4\\ &4x+3y+1z=7 \end{array} \end{align*}
This system has an infinite number of solutions: for any number $$a$$, $$x=a$$, $$y=\tfrac12(3-a)$$, $$z=4-\tfrac52a-\tfrac32$$ is a solution.
A boundary condition is analagous to adding an extra condition to give this system a unique solution, for example $$x=a=1$$. The usual way of imposing a boundary condition is to substitute the condition into our equations. In this example we would get:
\begin{align*} \begin{bmatrix} 2&0\\ 1&1\\ 3&1 \end{bmatrix}\mathbf{y} &= \begin{pmatrix} 2\\1\\3 \end{pmatrix} &&\text{or}& \begin{array}{3} &2y+0z=2\\ &1y+1z=1\\ &3y+1z=3 \end{array} \end{align*}
We can then remove one of these equations to leave a square, invertible matrix. For example, dropping the middle equation gives:
\begin{align*} \begin{bmatrix} 2&0\\ 3&1 \end{bmatrix}\mathbf{y} &= \begin{pmatrix} 2\\3 \end{pmatrix} &&\text{or}& \begin{array}{3} &2y+0z=2\\ &3y+1z=3 \end{array} \end{align*}
This problem now has one unique solution ($$y=1$$, $$z=0$$).

### Weakly imposing boundary conditions

To weakly impose a boundary conditions, a different approach is taken: instead of substituting (for example) $$x=1$$ into our system, we add $$x$$ to one side of an equation and we add $$1$$ to the other side. Doing this to the first equation gives:
\begin{align*} \begin{bmatrix} 2&2&0\\ 3&1&1\\ 4&3&1 \end{bmatrix}\mathbf{x} &= \begin{pmatrix} 4\\4\\7 \end{pmatrix} &&\text{or}& \begin{array}{3} &2x+2y+0z=4\\ &3x+1y+1z=4\\ &4x+3y+1z=7 \end{array} \end{align*}
This system now has one unique solution ($$x=1$$, $$y=1$$, $$z=0$$).
In this example, weakly imposing the boundary condition seems worse than substituting, as it leads to a larger problem which will take longer to solve. This is true, but if you had a more complicated condition (eg $$\sin x=0.5$$ or $$\max(x,y)=2$$), it is not always possible to write the condition in a nice way that can be substituted in, but it is easy to weakly impose the condition (although the result will not always be a linear matrix system, but more on that in chapter 4).

### Analysis

In the third chapter of my thesis, we wrote out the derivations of formulations of the weak imposition of Dirichet, Neumann, mixed Dirichlet–Neumann, and Robin conditions on Laplace's equation: we limited our work in this chapter to Laplace's equation as it is easier to analyse than the Helmholtz equation.
Once the formulations were derived, we proved some results about them: the main result that this analysis leads to is the proof of a priori error bounds.
An a priori error bound tells you how big the difference between your approximation and the actual solution will be. These bounds are called a priori as they can be calculated before you solve the problem (as opposed to a posteriori error bounds that are calculated after solving the problem to give you an idea of how accurate you are and which parts of the solution are more or less accurate). Proving these bounds is important, as proving this kind of bound shows that your method will give a good approximation of the actual solution.
A priori error bounds look like this:
$$\left\|u-u_h\right\|\leqslant ch^{a}\left\|u\right\|$$
In this equation, $$u$$ is the solution of the actual PDE problem; $$u_h$$ is our appoximation; the $$h$$ that appears in $$u_h$$ and on the right-hand size is the length of the longest edge in the mesh we are using; and $$c$$ and $$a$$ are positive constants. The vertical lines $$\|\cdot\|$$ are a measurement of the size of a function.
Overall, the equation says that the size of the difference between the solution and our approximation (ie the error) is smaller than a constant times $$h^a$$ times the size of the solution. Because $$a$$ is positive, $$h^a$$ gets smaller as $$h$$ get smaller, and so if we make the triangles in our mesh smaller (and so have more of them), we will see our error getting smaller.
The value of the constant $$a$$ depends on the choices of discretisation spaces used: using the spaces in the previous chapter gives $$a$$ equal to $$\tfrac32$$.

### Numerical results

Using Bempp, we approximated the solution on a series of meshes with different values of $$h$$ to check that we do indeed get order $$\tfrac32$$ convergence. By plotting $$\log\left(\left\|u-u_h\right\|\right)$$ against $$\log h$$, we obtain a graph with gradient $$a$$ and can easily compare this gradient to a gradient of $$\tfrac32$$. Here are a couple of our results:
The errors of a Dirichlet problem on a cube (left) and a Robin problem on a sphere (right) as $$h$$ is decreased. The dashed lines show order $$\tfrac32$$ convergence.
Note that the $$\log h$$ axis is reversed, as I find these plots easier to interpret this way found. Pleasingly, in these numerical experiments, the order of convergence agrees with the a priori bounds that we proved in this chapter.

These results conclude chapter 3 of my thesis. Why not take a break and refill the following figure with hot liquid before reading on.
The solution of a mixed Dirichlet–Neumann problem on the interior of a mug, solved using weakly imposed boundary conditions.
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 PhD thesis, chapter 4

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