On a less serious note

So, I realised just a few minutes ago that I haven't written about chaotic attractors in all their splendour. An incredible oversight on my part. These objects are achingly beautiful and intricately complex in form. They arise in phase space (I believe I have covered this) in certain parameter regions of systems. Their fractal geometry means that dynamics is incredibly sensitive on them and while trajectories will follow the path of the attractor (duh! attracting :-) ), there is no predicting which exact part of the attractor it will land on and follow round or ultimately where it will end up. The attractor is fixed, the trajectories are not. A nice succinct explanation is available here.

The one that ties in closest to my work is the Lorenz attractor. The three dimensional system for describing turbulence gives birth to an amazing structure. This attractor is where the 'butterfly effect' expression originated. The attractor itself is geometrically between two and three dimensions and winds itself around two points.

The image (from wiki) shows exactly this. Where the trajectories 'cross' in this two-dimensional visualisation is actually where they layer over each other - manifolds cannot intersect (the unstable manifold exactly describes the attractor). The view is better in 3d. The fractal (Hausdorff) dimension of the attractor is about 2.06.

Other systems with strange attractors are the Rossler system and Henon map. This page gives a nice description and a few images.

As a side note: for those of you who read and enjoyed 'Harry Potter and the Methods of Rationality' or 'Luminosity' I would recommend this strangely named (but you'll understand all too soon) 'Baby Eating Aliens' also from the Less Wrong family and the (there is a theme here, no?) 'Harry Potter and the Wastelands of Time' if you like a bit of complex, epic writing with lots of action and magic!

Enjoy! Also, something to look forward to: I hope to have my poster available for you lot next time I post. :D

Doh! Almost forgot. One of the lecturers at my university (Hinke Osinga) actually crocheted the Lorenz stable manifold! If you want to know how, read this!

Incidentally, my project supervisor is the Bernd Krauskopf mentioned in the article. And yes, it is one of their Christmas decorations. ;)

Nonlinearity gets all the fun

Nonlinearity is inherent in most systems before you make an assumption and reduce the beautiful complexity down to a boring bog standard linear system. There is a section in Strogatz that shows probably the most interesting thing you can do with linear systems (Chapter 5 section 3 page 138 onwards). And I only read it once because linear systems were dull and pretty repetitive long before A level.

Nonlinearity is pretty easy to understand. Take for example square numbers. You can make 4 by either multiplying 2 and 2 or -2 and -2. This means that, under the square operator, 4 has two (well, four really, but two are identical to the other two) possible previous values. This makes squaring a number nonlinear because tracking back you do not know whether two 2s or two -2s made the 4 you see at the moment. This particular brand of nonlinearity is called noninvertibility. You still following me?

Good. Right, to take this further we call the previous values of each number pre-images and the operation, a mapping. Now the quadratic (squaring) mapping that I am examining is:

what happens to z when a=2 and I vary lambda. This mapping does funny things depending on what I set lambda to. If it is between 0 and 1 then it behaves just like the squaring operator - each value of z has two pre-images as I iterate the map but in a circle radius=1-lambda around C the values have no pre-images. Now if I make lambda greater than 1, some values have two pre-images as before but the z values in the circle now have four pre-images.

Still with me? We can take this further but placing this property on curves not just points. A curve is just a line on a graph (an infinite set of points). Instead of looking backwards as we did with the points, we instead go forwards and see how the curve changes. It's first image is a circle, say. This curve then becomes the pre-image of the set of points of the circle under the mapping. And so on, forwards in time. It helps to look at the image below:

You can see the curves collecting on (heading towards) one curve called a manifold (it's attracting). Now consider the relationship of the points and the curves. Because a curve is just a set of points. With noninvertibility inherent in the system, what happens it you take the manifold and reverse the mappings for all the points? Wild Chaos! Tracking the possible points and curves and how and why they interact is the next stage of my project - at least for certain ranges of values for C, a and lambda. This limitation is purely because my project has a time limit of a year.

I am currently working with a PhD student, Stefanie Hittmeyer (who incidentally produced the image above) who is working on the full problem in all its scope. She has been focused on the manifold part of it for the moment while I have been examining the fractal structure side. My work nicely compliments hers. This work is on the fore-front of chaos research. Now it may seem that all this has little value in the real world but the great thing about nonlinear and chaotic systems is that it can accurately mimic real world systems. This is a major advantage over linear systems because when taking a real world problem you have to reduce the picture you are looking at so you can describe it using the simplest tools. With this kind of research, no such reduction is necessary and you can understand the system in it's full complexity. So maybe it'll turn out that neurological signals in the brain will turn out to have similar properties to the mapping that I am examining and my research will contribute to a fuller understanding of how we came to have consciousness and help neurosurgeons get better at fixing the biochemical circuitry that makes us who we are. Or maybe, and more likely since this mapping is a reduction of the five dimensional Lorenz model, it will just help meteorologists get really, really good at predicting weather patterns.

Who knows? It's sort of like the laser - a solution created with no problem that has now become integral to our lifestyle. Just check out how many things it is used in! I hope you managed to follow all that to the end. If you have any specific questions please feel free to email me but I would much rather you follow that urge to take a deeper look yourself (even if that is just reading all the wiki pages). It's way more fun!